In [19]:
pwd
Out[19]:
'/Users/mohammedkhattab/Downloads/PhD_scProject/scgast_Project'
In [13]:
from IPython.display import Image, display
display(Image(filename='Science.png'))
No description has been provided for this image

Tyser's et al. (2021)

Introduction¶

- In this notebook we will re-visit the data from the paper titled Single-cell transcriptomic characterization of a gastrulating human embryo Tyser et al. (2021), where we will explore the data of the 1195 single cell from embryonic stage Carnegie Stage 7 (CS7) around the third week after fertilization, explore the present cell types and plot them in UMAP plots (Uniform Manifold Approximation and Projection), eventually, we will use the R package infercnv to identify any type of copy number variations in comparison to the most basal cell type in the sample which is the Epiblast, and see how single cell data can tell us about it.¶

Chapter 1: Packages import and data upload¶

In [14]:
import numpy as np
import pandas as pd
import anndata as ad
import pyreadr
In [15]:
import scanpy as sc
import urllib.request
from pathlib import Path
import os
In [ ]:
import matplotlib.pyplot as plt
from scipy.sparse import csr_matrix
import scarches as sca
import scanorama
from scipy.io import mmwrite
In [ ]:
import anndata2ri
from rpy2.robjects import conversion
from rpy2.robjects.conversion import localconverter
In [ ]:
with localconverter(anndata2ri.converter):
    %reload_ext rpy2.ipython

sc.settings.verbosity = 3
sc.logging.print_header()
sc.settings.set_figure_params(dpi=80, facecolor="white")

R libraries for later¶

In [ ]:
%%R
library(reticulate)
library(Seurat)
library(tidyverse)
library(R.utils) 
library(devtools)
library(tidyverse)
library(Matrix)
library(infercnv)

- The expression matrix file raw_read.rds and, the UMAP data table containing the cell names umap.rds files, that can be found in the paper's github page. Download them through this link if not already present in your directory.¶

In [ ]:
# Load .rds file provided by the authors that contains the expression values
raw_reads = pyreadr.read_r("raw_reads.rds")
print(raw_reads.keys())
In [ ]:
# Extract the expression values from the .rds object
raw_reads = raw_reads[None]
print(raw_reads.shape) #(1195, 57490)
raw_reads.head(2) 

UMAP file upload¶

- raw_reads lacks the cell names, luckily they are provided in another object by the authors called umap, this dataframe contains some useful data, but most importantly it contains the same order of cell names as the raw_reads object, enabling the export of the cell names to the counts object.¶

In [7]:
# Load .rds file 'umap'
umap = pyreadr.read_r("umap.rds")
# Inspect keys
print(umap.keys()) # odict_keys([None])
umap = umap[None]
print(umap.shape) # (1195, 6)
odict_keys([None])
(1195, 6)
In [8]:
#make cell names the index for the `umap`object
umap = umap.set_index("cell_name")
umap.head(2)
Out[8]:
X X0 X1 cluster_id sub_cluster
cell_name
SS.sc7785290 0 12.213498 -0.550328 Hemogenic Endothelial Progenitors Blood Progenitors
SS.sc7786612 1 2.404149 -7.389468 Endoderm DE(P)
In [ ]:
#Now we can add the cell names to the raw_reads object using set_index() function
raw_reads_indexed = raw_reads.set_index(umap.index)
#raw_reads_indexed
In [ ]:
umap.index == raw_reads_indexed.index

Metadata upload¶

This 'metadata' file E-MTAB-9388.sdrf.txt is another file contains some important biological data, can be downloaded from here to continue the analysis if the file not already present.¶

Note: The file E-MTAB-9388.idf.txt found in the same link above is not necessary for this analysis.¶
In [15]:
# metadata upload
metadata = pd.read_csv("E-MTAB-9388.zip", sep= "\t")
print(metadata.shape) #After investigating the dataframe, it seems that each cell is repeated once.
(2390, 41)
In [16]:
#We will remove the duplicates using drop_duplicates() function
metadata_clear = metadata.drop_duplicates(subset="Source Name")
In [17]:
#In order to unify the cell names we will change "_" to "." to match the `umap` dataframe we uploaded eariler
metadata_clear.loc[:, "Source Name"] = metadata_clear["Source Name"].str.replace("_", ".", regex=False)
In [18]:
#Make it an Index
metadata_clear = metadata_clear.set_index("Source Name")
# Reindex the umap DataFrame to match the metadata_clear index
umap = umap.reindex(metadata_clear.index)
# Add the annotation column `cluster_id`for later
metadata_clear["cluster_id"] = umap["cluster_id"]

Chapter 2: Create AnnData Object¶

In [19]:
# Build AnnData object
adata = ad.AnnData(
    X= raw_reads_indexed.values,          # expression matrix
    obs= metadata_clear,                  # cell metadata
    var= pd.DataFrame(index=raw_reads_indexed.columns)  # gene metadata
)
In [20]:
print(adata)   # should print: AnnData object with n_obs × n_vars = 1195 × 57490
AnnData object with n_obs × n_vars = 1195 × 57490
    obs: 'Comment[ENA_SAMPLE]', 'Comment[BioSD_SAMPLE]', 'Characteristics[organism]', 'Characteristics[developmental stage]', 'Characteristics[age]', 'Unit[time unit]', 'Characteristics[individual]', 'Characteristics[sex]', 'Characteristics[organism part]', 'Characteristics[sampling site]', 'Characteristics[inferred cell type - authors labels]', 'Characteristics[inferred cell type - ontology labels]', 'Material Type', 'Protocol REF', 'Protocol REF.1', 'Protocol REF.2', 'Extract Name', 'Comment[LIBRARY_LAYOUT]', 'Comment[LIBRARY_SELECTION]', 'Comment[LIBRARY_SOURCE]', 'Comment[LIBRARY_STRATEGY]', 'Comment[NOMINAL_LENGTH]', 'Comment[NOMINAL_SDEV]', 'Comment[end bias]', 'Comment[input molecule]', 'Comment[library construction]', 'Comment[primer]', 'Comment[single cell isolation]', 'Comment[spike in]', 'Protocol REF.3', 'Performer', 'Assay Name', 'Technology Type', 'Comment[ENA_EXPERIMENT]', 'Scan Name', 'Comment[SUBMITTED_FILE_NAME]', 'Comment[ENA_RUN]', 'Comment[FASTQ_URI]', 'Factor Value[single cell identifier]', 'Factor Value[inferred cell type - ontology labels]', 'cluster_id'

AnnData object with n_obs × n_vars = 1195 × 57490 obs: 'Comment[ENA_SAMPLE]', 'Comment[BioSD_SAMPLE]', 'Characteristics[organism]', 'Characteristics[developmental stage]', 'Characteristics[age]', 'Unit[time unit]', 'Characteristics[individual]', 'Characteristics[sex]', 'Characteristics[organism part]', 'Characteristics[sampling site]', 'Characteristics[inferred cell type - authors labels]', 'Characteristics[inferred cell type - ontology labels]', 'Material Type', 'Protocol REF', 'Protocol REF.1', 'Protocol REF.2', 'Extract Name', 'Comment[LIBRARY_LAYOUT]', 'Comment[LIBRARY_SELECTION]', 'Comment[LIBRARY_SOURCE]', 'Comment[LIBRARY_STRATEGY]', 'Comment[NOMINAL_LENGTH]', 'Comment[NOMINAL_SDEV]', 'Comment[end bias]', 'Comment[input molecule]', 'Comment[library construction]', 'Comment[primer]', 'Comment[single cell isolation]', 'Comment[spike in]', 'Protocol REF.3', 'Performer', 'Assay Name', 'Technology Type', 'Comment[ENA_EXPERIMENT]', 'Scan Name', 'Comment[SUBMITTED_FILE_NAME]', 'Comment[ENA_RUN]', 'Comment[FASTQ_URI]', 'Factor Value[single cell identifier]', 'Factor Value[inferred cell type - ontology labels]', 'cluster_id'

In [22]:
# Start the standard workflow for Anndata objects
adata.var_names_make_unique()
sc.pp.calculate_qc_metrics(adata, inplace=True)
In [23]:
#Check the distribution of counts across cells
sc.pl.violin(adata,['total_counts', 'n_genes_by_counts'], multi_panel= True)
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The violin plot tell us that our cell population doesn't require further cutting, since the cells are well distributed, no outliers and to maintain as much cells as possible for the further analysis.¶

In [24]:
# Normalizing to median total counts
sc.pp.normalize_total(adata)
sc.pp.log1p(adata)
normalizing counts per cell
    finished (0:00:00)
In [25]:
sc.pl.highest_expr_genes(adata, n_top = 10, )
normalizing counts per cell
    finished (0:00:00)
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In [ ]:
# Perform the standard scaling and dimentionality reduction workflow
sc.pp.scale(adata, max_value=10)
sc.tl.pca(adata, n_comps=30, random_state=42)
sc.pp.neighbors(adata, n_neighbors=10, n_pcs=30, random_state=42)
sc.tl.umap(adata, random_state=42)
In [ ]:
# Annotate highly variable genes in the `adata`object
sc.pp.highly_variable_genes(adata)
sc.pl.highly_variable_genes(adata)
In [ ]:
#Perform leiden clustering with resolution = 0.75
sc.tl.leiden(adata, resolution=0.75, flavor="igraph", n_iterations=2, random_state=42)
In [ ]:
fig, axs = plt.subplots(1, 2, figsize=(10, 5), dpi=120)

sc.pl.umap(adata, color="leiden", ax=axs[0], show=False)
sc.pl.umap(adata, color="pct_counts_in_top_50_genes", ax=axs[1], show=False)
plt.show()
In [430]:
adata.obs
Out[430]:
Comment[ENA_SAMPLE] Comment[BioSD_SAMPLE] Characteristics[organism] Characteristics[developmental stage] Characteristics[age] Unit[time unit] Characteristics[individual] Characteristics[sex] Characteristics[organism part] Characteristics[sampling site] ... n_genes_by_counts log1p_n_genes_by_counts total_counts log1p_total_counts pct_counts_in_top_50_genes pct_counts_in_top_100_genes pct_counts_in_top_200_genes pct_counts_in_top_500_genes leiden AnnotatedCluster
Source Name
SS.sc7785278 ERS5181934 SAMEA7423586 Homo sapiens embryo 16 to 19 day CS7 male whole organism yolk sac ... 4922 8.501673 377151.968991 12.840406 23.622851 30.429528 39.508748 55.256619 0 HEP
SS.sc7785279 ERS5181935 SAMEA7423587 Homo sapiens embryo 16 to 19 day CS7 male whole organism yolk sac ... 6942 8.845489 259888.990001 12.468014 18.311227 25.105456 33.482184 48.358286 1 Primative Streak
SS.sc7785280 ERS5181936 SAMEA7423588 Homo sapiens embryo 16 to 19 day CS7 male whole organism yolk sac ... 6140 8.722743 437911.014986 12.989773 16.438116 23.168028 31.973973 48.713081 2 Emergent Mesoderm
SS.sc7785281 ERS5181937 SAMEA7423589 Homo sapiens embryo 16 to 19 day CS7 male whole organism yolk sac ... 3800 8.243019 322351.983054 12.683402 18.344805 27.028758 38.095824 57.775552 3 Nascent Mesoderm
SS.sc7785282 ERS5181938 SAMEA7423590 Homo sapiens embryo 16 to 19 day CS7 male whole organism yolk sac ... 2964 7.994632 394318.996000 12.884918 25.350752 33.529113 43.988398 62.262868 4 Extra-embryonic Mesoderm
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
SS.sc7788385 ERS5183125 SAMEA7424905 Homo sapiens embryo 16 to 19 day CS7 male whole organism caudal ... 6361 8.758098 226569.027995 12.330809 17.380574 24.134041 32.566881 48.247448 7 Advanced Mesoderm
SS.sc7788387 ERS5183126 SAMEA7424906 Homo sapiens embryo 16 to 19 day CS7 male whole organism caudal ... 4647 8.444192 353921.980998 12.776835 17.275106 24.978208 35.842762 54.734178 3 Nascent Mesoderm
SS.sc7788388 ERS5183127 SAMEA7424907 Homo sapiens embryo 16 to 19 day CS7 male whole organism caudal ... 2822 7.945555 366998.009999 12.813114 19.084758 28.550866 41.422998 63.511382 3 Nascent Mesoderm
SS.sc7788390 ERS5183128 SAMEA7424908 Homo sapiens embryo 16 to 19 day CS7 male whole organism caudal ... 6110 8.717846 459601.642974 13.038118 19.059282 26.192770 35.600091 51.565122 7 Advanced Mesoderm
SS.sc7788391 ERS5183129 SAMEA7424909 Homo sapiens embryo 16 to 19 day CS7 male whole organism yolk sac ... 5612 8.632841 349231.986990 12.763495 17.768009 24.528315 33.272577 49.325280 9 Epiblast

1195 rows × 51 columns

Chapter 3: Cell type annotation¶

In [368]:
# Find marker genes for each of leiden cluster 
sc.tl.rank_genes_groups(adata, 'leiden', method='logreg')
sc.pl.rank_genes_groups(adata, n_genes=20, sharey=False)
ranking genes
    finished: added to `.uns['rank_genes_groups']`
    'names', sorted np.recarray to be indexed by group ids
    'scores', sorted np.recarray to be indexed by group ids
 (0:00:02)
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The following kernel contains a handful of common markers from Supplementary Note 1 - Annotation of gastrula cell types.¶

In [369]:
markers = ["leiden", "GATA1", "TBXT", "MSGN1", "MESP1", "MEF2C", 
           "LEFTY2", "FOXF1", "HAND1", "FOXA2", "GATA6", "HOXA1", 
           "CDH1", "FST", "DLX5", "SOX2"]

# Create subplots
fig, axs = plt.subplots(8, 2, figsize=(20, 40), dpi=120)

for i, marker in enumerate(markers):
    row = i // 2
    col = i % 2
    sc.pl.umap(adata, color=marker, ax=axs[row, col], show=False)

plt.show()
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Dotplot for the top markers¶

In [370]:
# Extract ranked marker genes
marker_df = sc.get.rank_genes_groups_df(adata, group=None)
In [ ]:
top_markers = marker_df.groupby("group", observed=False).head() 

sc.pl.dotplot(
    adata,
    var_names=top_markers['names'].unique().tolist(),
    groupby="leiden",
    standard_scale="var",
    dendrogram=True
)

Identifying main markers in each cluster, thus representing its cell type.¶

In [ ]:
marker_genes = {
    'HEP': ['SPI1', 'MEF2C'],
    'Primative Streak': ['TBXT', 'CDH1' , 'FST'],
    'Emergent Mesoderm': ['MESP1', 'LHX1', 'LEFTY2'],
    'Nascent Mesoderm': ['TBXT', 'MESP1', 'MSGN1'],
    'Extra-embryonic Mesoderm': ['FOXF1', 'HAND1'],
    'Axial Mesoderm': ['TBXT', 'FOXA2', 'CDH1'],
    'Erythrocytes': ['GATA1', 'HBZ', 'HBE1'],
    'Advanced Mesoderm': ['MESP1', 'PDGFRA','BMP4', 'SNAI2', "HAND1", "GATA6"],
    'Endoderm': ['SOX17', 'FOXA2', 'CXCR4', 'TMA7'],
    'Epiblast' : ['SOX2', 'OTX2', 'CDH1'],
    'Ectoderm (Amniotic/Embryonic)' : ['DLX5', 'TFAP2A', 'GATA3'],
    'Caudal Ad. M  and PS / N. M': "HOXA1"
}

sc.pl.dotplot(
    adata,
    marker_genes,
    groupby="leiden",
    standard_scale="var",
    dendrogram=True
)

The heatmap above is a little messy but this is should be expected, as lots of cells share multiple important markers, but you ultimately each cell type will end up with its unique set of markers.¶

In [353]:
# Create a mapping dictionary for cell types
cluster2celltype = {
    "0" :'HEP',
    "1" :'Primative Streak',
    "2" :'Emergent Mesoderm',
    "3" :'Nascent Mesoderm',
    "4" :'Extra-embryonic Mesoderm',
    "5" :'Axial Mesoderm',
    "6" :'Erythrocytes',
    "7" :'Advanced Mesoderm',
    "8" :'Endoderm',
    "9" :'Epiblast',
    "10" :'Caudal Ad. Mesoderm  and PS / Nascent Mesoderm',
    "11" :'Ectoderm (Amniotic/Embryonic)'
}
In [354]:
# Add a new column with annotations
adata.obs["AnnotatedCluster"] = adata.obs["leiden"].map(cluster2celltype)
In [355]:
sc.tl.leiden(adata, resolution= 0.75, flavor="igraph", n_iterations=2)

fig, axs = plt.subplots(1, 2, figsize=(10,3), dpi=220)
sc.pl.umap(adata, color="leiden", ax=axs[0], show=False)
sc.pl.umap(adata, color="AnnotatedCluster", ax=axs[1], show=False)
plt.show()
running Leiden clustering
    finished: found 14 clusters and added
    'leiden', the cluster labels (adata.obs, categorical) (0:00:00)
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Fig. 1c from reference paper for comparison.¶

In [387]:
display(Image(filename='Fig1c.png', width=700, height=300))
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Optional step: h5ad to seurat¶

If wanted save a version of the AnnData object¶

adata.write("adata_obj.h5ad")

Chapter 4: InferCNV for copy number variations analysis¶

Create metadata file¶

In [62]:
#add metadata
adata.obs.to_csv("metadata.csv")
In [63]:
%%R
# read the metadata in R
metadata <- read_csv("metadata.csv")
metadata
Rows: 1195 Columns: 52
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (41): Source Name, Comment[ENA_SAMPLE], Comment[BioSD_SAMPLE], Character...
dbl (11): Comment[NOMINAL_LENGTH], Comment[NOMINAL_SDEV], n_genes_by_counts,...

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
# A tibble: 1,195 × 52
   `Source Name` `Comment[ENA_SAMPLE]` `Comment[BioSD_SAMPLE]`
   <chr>         <chr>                 <chr>                  
 1 SS.sc7785278  ERS5181934            SAMEA7423586           
 2 SS.sc7785279  ERS5181935            SAMEA7423587           
 3 SS.sc7785280  ERS5181936            SAMEA7423588           
 4 SS.sc7785281  ERS5181937            SAMEA7423589           
 5 SS.sc7785282  ERS5181938            SAMEA7423590           
 6 SS.sc7785283  ERS5181939            SAMEA7423591           
 7 SS.sc7785286  ERS5181940            SAMEA7423592           
 8 SS.sc7785288  ERS5181941            SAMEA7423593           
 9 SS.sc7785289  ERS5181942            SAMEA7423594           
10 SS.sc7785290  ERS5181943            SAMEA7423595           
# ℹ 1,185 more rows
# ℹ 49 more variables: `Characteristics[organism]` <chr>,
#   `Characteristics[developmental stage]` <chr>, `Characteristics[age]` <chr>,
#   `Unit[time unit]` <chr>, `Characteristics[individual]` <chr>,
#   `Characteristics[sex]` <chr>, `Characteristics[organism part]` <chr>,
#   `Characteristics[sampling site]` <chr>,
#   `Characteristics[inferred cell type - authors labels]` <chr>, …
# ℹ Use `print(n = ...)` to see more rows

Create raw_counts.tsv¶

Seurat handel the counts when the genes represented as rownames.¶

In [64]:
#We have to transpose the matrix.
raw_reads_indexed_transposed = raw_reads_indexed.T

Create raw_counts and cell cell-type annotation file for inferCNV¶

In [65]:
annotation_file = adata.obs[['AnnotatedCluster']].copy()
#annotation_file
In [66]:
#Save raw_counts as raw_counts.tsv 
raw_reads_indexed_transposed.to_csv("raw_counts.tsv", sep="\t")
#Save cell cell-type annotation file as cell_annotations.tsv
annotation_file.to_csv("cell_annotations.tsv", sep="\t", header=False)
#raw_reads_indexed_transposed

Re-upload the data in R as tsv files¶

In [67]:
%%R
raw_counts <- read.table("raw_counts.tsv", sep="\t", header=TRUE, row.names=1, check.names=FALSE)
head(rownames(raw_counts))
[1] "A1BG"     "A1BG.AS1" "A1CF"     "A2M"      "A2M.AS1"  "A2ML1"   
In [68]:
%%R
annotation_file <- read_tsv("cell_annotations.tsv")
annotation_file
Rows: 1194 Columns: 2
── Column specification ────────────────────────────────────────────────────────
Delimiter: "\t"
chr (2): SS.sc7785278, HEP

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
# A tibble: 1,194 × 2
   SS.sc7785278 HEP                     
   <chr>        <chr>                   
 1 SS.sc7785279 Primative Streak        
 2 SS.sc7785280 Nascent Mesoderm        
 3 SS.sc7785281 Extra-embryonic Mesoderm
 4 SS.sc7785282 Axial Mesoderm          
 5 SS.sc7785283 Emergent Mesoderm       
 6 SS.sc7785286 HEP                     
 7 SS.sc7785288 Nascent Mesoderm        
 8 SS.sc7785289 Primative Streak        
 9 SS.sc7785290 Extra-embryonic Mesoderm
10 SS.sc7785291 Axial Mesoderm          
# ℹ 1,184 more rows
# ℹ Use `print(n = ...)` to see more rows

For gene position data file required for inferCNV the authors have provided the file in this link, you can download it, if it is not present in your current directory.¶

In [405]:
%%R
#Gene order file found online
gene_order <- read_tsv("gencode_v19_gene_pos.txt")
gene_order
Rows: 55764 Columns: 4
── Column specification ────────────────────────────────────────────────────────
Delimiter: "\t"
chr (2): DDX11L1, chr1
dbl (2): 11869, 14412

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
# A tibble: 55,764 × 4
   DDX11L1      chr1  `11869` `14412`
   <chr>        <chr>   <dbl>   <dbl>
 1 WASH7P       chr1    14363   29806
 2 MIR1302-11   chr1    29554   31109
 3 FAM138A      chr1    34554   36081
 4 OR4G4P       chr1    52473   54936
 5 OR4G11P      chr1    62948   63887
 6 OR4F5        chr1    69091   70008
 7 RP11-34P13.7 chr1    89295  133566
 8 RP11-34P13.8 chr1    89551   91105
 9 CICP27       chr1   131025  134836
10 AL627309.1   chr1   134901  139379
# ℹ 55,754 more rows
# ℹ Use `print(n = ...)` to see more rows
In [406]:
%%R
#Add column names for the file
colnames(gene_order) <- c("gene","chr","start","end")
In [407]:
%%R
# How many overlap
length(intersect(rownames(raw_counts), gene_order$gene))
[1] 30222
In [408]:
%%R
# Optional: save clean version for inferCNV (no header, 4 cols)
write.table(gene_order, "gene_order.tsv",
            sep="\t", quote=FALSE, row.names=FALSE, col.names=FALSE)
In [410]:
%%R
#make sure the gene_order have same genes as in raw_counts to prevent errors
gene_order_updated <- subset(gene_order, gene %in% rownames(raw_counts))

gene_order_updated <- gene_order_updated[match(rownames(raw_counts), gene_order_updated$gene), ]

# Remove any rows with newly emergent NA values from the subsetting step
gene_order_updated <- na.omit(gene_order_updated)

dim(gene_order_updated)
[1] 30222     4

Refine the genes present, their intersection with raw_counts and their order.¶

In [411]:
%%R
# Keep only genes that exist in gene_order
common_genes <- intersect(rownames(raw_counts), gene_order_updated$gene)
# Subset expression matrix
raw_counts <- raw_counts[common_genes, ]
# Subset gene order and keep the same order as counts
gene_order_final <- gene_order_updated[match(rownames(raw_counts), gene_order_updated$gene), ]
In [412]:
%%R
# check
all(rownames(raw_counts) == gene_order_final$gene)
[1] TRUE
In [77]:
%%R
# Save a clean version for inferCNV (no header, 4 cols)
write.table(gene_order, "gene_order_final.tsv",
            sep="\t", quote=FALSE, row.names=FALSE, col.names=FALSE)

InferCNV¶

In [80]:
%%R
infercnv_obj <- CreateInfercnvObject(raw_counts_matrix = as.matrix(raw_counts),
                                     annotations_file = "cell_annotations.tsv",
                                     gene_order_file = "gene_order_final.tsv",
                                     delim = "\t",
                                     ref_group_names = "Epiblast" )
INFO [2025-10-08 15:46:07] Parsing gene order file: gene_order_final.tsv
INFO [2025-10-08 15:46:07] Parsing cell annotations file: cell_annotations.tsv
INFO [2025-10-08 15:46:07] ::order_reduce:Start.
INFO [2025-10-08 15:46:07] .order_reduce(): expr and order match.
INFO [2025-10-08 15:46:07] ::process_data:order_reduce:Reduction from positional data, new dimensions (r,c) = 30222,1195 Total=-174820.018278072 Min=-10 Max=10.
INFO [2025-10-08 15:46:07] num genes removed taking into account provided gene ordering list: 1657 = 5.4827609026537% removed.
INFO [2025-10-08 15:46:07] -filtering out cells < 100 or > Inf, removing 51.8828 % of cells
WARN [2025-10-08 15:46:07] Please use "options(scipen = 100)" before running infercnv if you are using the analysis_mode="subclusters" option or you may encounter an error while the hclust is being generated.
INFO [2025-10-08 15:46:09] validating infercnv_obj
In [81]:
%%R
infercnv_obj_run <- infercnv::run(
    infercnv_obj,
    out_dir = "output_dir",
    cutoff = 0,          # instead of 1, keeps more genes
    min_cells_per_gene = 5, # relax cell filter
    HMM = T,
    per_chr_hmm_subclusters=TRUE,
    HMM_type="i3",
    analysis_mode="subclusters", #inferCNV will attempt to find subpopulations with distinct CNV patterns, rather than assuming each provided group is uniform
    denoise = T)
INFO [2025-10-08 15:46:12] ::process_data:Start
INFO [2025-10-08 15:46:12] Creating output path output_dir
INFO [2025-10-08 15:46:12] Checking for saved results.
INFO [2025-10-08 15:46:12] 

	STEP 1: incoming data

INFO [2025-10-08 15:46:19] 

	STEP 02: Removing lowly expressed genes

INFO [2025-10-08 15:46:19] ::above_min_mean_expr_cutoff:Start
INFO [2025-10-08 15:46:19] Removing 6708 genes from matrix as below mean expr threshold: 0
INFO [2025-10-08 15:46:19] validating infercnv_obj
INFO [2025-10-08 15:46:19] There are 21857 genes and 575 cells remaining in the expr matrix.
INFO [2025-10-08 15:46:20] Removed 7850 genes having fewer than 5 min cells per gene = 35.9153 % genes removed here
INFO [2025-10-08 15:46:20] validating infercnv_obj
INFO [2025-10-08 15:46:24] 

	STEP 03: normalization by sequencing depth

INFO [2025-10-08 15:46:24] normalizing counts matrix by depth
INFO [2025-10-08 15:46:24] Computed total sum normalization factor as median libsize: 1565.062421
INFO [2025-10-08 15:46:28] 

	STEP 04: log transformation of data

INFO [2025-10-08 15:46:28] transforming log2xplus1()
INFO [2025-10-08 15:46:32] 

	STEP 08: removing average of reference data (before smoothing)

INFO [2025-10-08 15:46:32] ::subtract_ref_expr_from_obs:Start inv_log=FALSE, use_bounds=TRUE
INFO [2025-10-08 15:46:32] subtracting mean(normal) per gene per cell across all data
INFO [2025-10-08 15:46:34] -subtracting expr per gene, use_bounds=TRUE
INFO [2025-10-08 15:46:38] 

	STEP 09: apply max centered expression threshold: 3

INFO [2025-10-08 15:46:38] ::process_data:setting max centered expr, threshold set to: +/-:  3
INFO [2025-10-08 15:46:41] 

	STEP 10: Smoothing data per cell by chromosome

INFO [2025-10-08 15:46:41] smooth_by_chromosome: chr: chr1
INFO [2025-10-08 15:46:42] smooth_by_chromosome: chr: chr2
INFO [2025-10-08 15:46:42] smooth_by_chromosome: chr: chr3
INFO [2025-10-08 15:46:42] smooth_by_chromosome: chr: chr4
INFO [2025-10-08 15:46:43] smooth_by_chromosome: chr: chr5
INFO [2025-10-08 15:46:43] smooth_by_chromosome: chr: chr6
INFO [2025-10-08 15:46:44] smooth_by_chromosome: chr: chr7
INFO [2025-10-08 15:46:44] smooth_by_chromosome: chr: chr8
INFO [2025-10-08 15:46:45] smooth_by_chromosome: chr: chr9
INFO [2025-10-08 15:46:45] smooth_by_chromosome: chr: chr10
INFO [2025-10-08 15:46:46] smooth_by_chromosome: chr: chr11
INFO [2025-10-08 15:46:46] smooth_by_chromosome: chr: chr12
INFO [2025-10-08 15:46:46] smooth_by_chromosome: chr: chr13
INFO [2025-10-08 15:46:47] smooth_by_chromosome: chr: chr14
INFO [2025-10-08 15:46:48] smooth_by_chromosome: chr: chr15
INFO [2025-10-08 15:46:48] smooth_by_chromosome: chr: chr16
INFO [2025-10-08 15:46:48] smooth_by_chromosome: chr: chr17
INFO [2025-10-08 15:46:49] smooth_by_chromosome: chr: chr18
INFO [2025-10-08 15:46:50] smooth_by_chromosome: chr: chr19
INFO [2025-10-08 15:46:50] smooth_by_chromosome: chr: chr20
INFO [2025-10-08 15:46:50] smooth_by_chromosome: chr: chr21
INFO [2025-10-08 15:46:50] smooth_by_chromosome: chr: chr22
INFO [2025-10-08 15:46:56] 

	STEP 11: re-centering data across chromosome after smoothing

INFO [2025-10-08 15:46:56] ::center_smooth across chromosomes per cell
INFO [2025-10-08 15:47:01] 

	STEP 12: removing average of reference data (after smoothing)

INFO [2025-10-08 15:47:01] ::subtract_ref_expr_from_obs:Start inv_log=FALSE, use_bounds=TRUE
INFO [2025-10-08 15:47:01] subtracting mean(normal) per gene per cell across all data
INFO [2025-10-08 15:47:03] -subtracting expr per gene, use_bounds=TRUE
INFO [2025-10-08 15:47:08] 

	STEP 14: invert log2(FC) to FC

INFO [2025-10-08 15:47:08] invert_log2(), computing 2^x
INFO [2025-10-08 15:47:16] 

	STEP 15: computing tumor subclusters via leiden

INFO [2025-10-08 15:47:16] define_signif_tumor_subclusters(p_val=0.1
INFO [2025-10-08 15:47:16] define_signif_tumor_subclusters(), tumor: Advanced Mesoderm
INFO [2025-10-08 15:47:16] Setting auto leiden resolution for Advanced Mesoderm to 0.3038
INFO [2025-10-08 15:47:17] define_signif_tumor_subclusters(), tumor: Axial Mesoderm
INFO [2025-10-08 15:47:17] Setting auto leiden resolution for Axial Mesoderm to 0.379855
INFO [2025-10-08 15:47:18] define_signif_tumor_subclusters(), tumor: Emergent Mesoderm
INFO [2025-10-08 15:47:18] Less cells in group Emergent Mesoderm than k_nn setting. Keeping as a single subcluster.
INFO [2025-10-08 15:47:18] define_signif_tumor_subclusters(), tumor: Endoderm
INFO [2025-10-08 15:47:18] Setting auto leiden resolution for Endoderm to 0.321105
INFO [2025-10-08 15:47:19] define_signif_tumor_subclusters(), tumor: Erythrocytes
INFO [2025-10-08 15:47:19] Setting auto leiden resolution for Erythrocytes to 0.217096
INFO [2025-10-08 15:47:20] define_signif_tumor_subclusters(), tumor: Extra-embryonic Mesoderm
INFO [2025-10-08 15:47:20] Setting auto leiden resolution for Extra-embryonic Mesoderm to 0.172198
INFO [2025-10-08 15:47:21] define_signif_tumor_subclusters(), tumor: HEP
INFO [2025-10-08 15:47:21] Setting auto leiden resolution for HEP to 0.333884
INFO [2025-10-08 15:47:23] define_signif_tumor_subclusters(), tumor: Nascent Mesoderm
INFO [2025-10-08 15:47:23] Setting auto leiden resolution for Nascent Mesoderm to 0.184162
INFO [2025-10-08 15:47:24] define_signif_tumor_subclusters(), tumor: Primative Streak
INFO [2025-10-08 15:47:24] Setting auto leiden resolution for Primative Streak to 0.231139
INFO [2025-10-08 15:47:25] define_signif_tumor_subclusters(), tumor: Epiblast
INFO [2025-10-08 15:47:25] Setting auto leiden resolution for Epiblast to 0.202492
INFO [2025-10-08 15:47:26] Less cells in group Emergent Mesoderm than k_nn setting. Keeping as a single per chr subcluster.
INFO [2025-10-08 15:47:26] Less cells in group Emergent Mesoderm than k_nn setting. Keeping as a single per chr subcluster.
INFO [2025-10-08 15:47:26] Less cells in group Emergent Mesoderm than k_nn setting. Keeping as a single per chr subcluster.
INFO [2025-10-08 15:47:27] Less cells in group Emergent Mesoderm than k_nn setting. Keeping as a single per chr subcluster.
INFO [2025-10-08 15:47:27] Less cells in group Emergent Mesoderm than k_nn setting. Keeping as a single per chr subcluster.
INFO [2025-10-08 15:47:27] Less cells in group Emergent Mesoderm than k_nn setting. Keeping as a single per chr subcluster.
INFO [2025-10-08 15:47:27] Less cells in group Emergent Mesoderm than k_nn setting. Keeping as a single per chr subcluster.
INFO [2025-10-08 15:47:27] Less cells in group Emergent Mesoderm than k_nn setting. Keeping as a single per chr subcluster.
INFO [2025-10-08 15:47:27] Less cells in group Emergent Mesoderm than k_nn setting. Keeping as a single per chr subcluster.
INFO [2025-10-08 15:47:27] Less cells in group Emergent Mesoderm than k_nn setting. Keeping as a single per chr subcluster.
INFO [2025-10-08 15:47:27] Less cells in group Emergent Mesoderm than k_nn setting. Keeping as a single per chr subcluster.
INFO [2025-10-08 15:47:27] Less cells in group Emergent Mesoderm than k_nn setting. Keeping as a single per chr subcluster.
INFO [2025-10-08 15:47:27] Less cells in group Emergent Mesoderm than k_nn setting. Keeping as a single per chr subcluster.
INFO [2025-10-08 15:47:27] Less cells in group Emergent Mesoderm than k_nn setting. Keeping as a single per chr subcluster.
INFO [2025-10-08 15:47:27] Less cells in group Emergent Mesoderm than k_nn setting. Keeping as a single per chr subcluster.
INFO [2025-10-08 15:47:27] Less cells in group Emergent Mesoderm than k_nn setting. Keeping as a single per chr subcluster.
INFO [2025-10-08 15:47:27] Less cells in group Emergent Mesoderm than k_nn setting. Keeping as a single per chr subcluster.
INFO [2025-10-08 15:47:27] Less cells in group Emergent Mesoderm than k_nn setting. Keeping as a single per chr subcluster.
INFO [2025-10-08 15:47:27] Less cells in group Emergent Mesoderm than k_nn setting. Keeping as a single per chr subcluster.
INFO [2025-10-08 15:47:28] Less cells in group Emergent Mesoderm than k_nn setting. Keeping as a single per chr subcluster.
INFO [2025-10-08 15:47:28] Less cells in group Emergent Mesoderm than k_nn setting. Keeping as a single per chr subcluster.
INFO [2025-10-08 15:47:28] Less cells in group Emergent Mesoderm than k_nn setting. Keeping as a single per chr subcluster.
INFO [2025-10-08 15:47:35] ::plot_cnv:Start
INFO [2025-10-08 15:47:35] ::plot_cnv:Current data dimensions (r,c)=14007,575 Total=8056179.15098159 Min=0.827673338207976 Max=1.15777732284435.
INFO [2025-10-08 15:47:35] ::plot_cnv:Depending on the size of the matrix this may take a moment.
INFO [2025-10-08 15:47:35] plot_cnv(): auto thresholding at: (0.967126 , 1.033409)
INFO [2025-10-08 15:47:35] plot_cnv_observation:Start
INFO [2025-10-08 15:47:35] Observation data size: Cells= 498 Genes= 14007
INFO [2025-10-08 15:47:35] clustering observations via method: ward.D
INFO [2025-10-08 15:47:35] Number of cells in group(1) is 47
INFO [2025-10-08 15:47:35] group size being clustered:  47,14007
INFO [2025-10-08 15:47:35] Number of cells in group(2) is 1
INFO [2025-10-08 15:47:35] Skipping group: 2, since less than 2 entries
INFO [2025-10-08 15:47:35] Number of cells in group(3) is 37
INFO [2025-10-08 15:47:35] group size being clustered:  37,14007
INFO [2025-10-08 15:47:35] Number of cells in group(4) is 9
INFO [2025-10-08 15:47:35] group size being clustered:  9,14007
INFO [2025-10-08 15:47:35] Number of cells in group(5) is 45
INFO [2025-10-08 15:47:35] group size being clustered:  45,14007
INFO [2025-10-08 15:47:35] Number of cells in group(6) is 67
INFO [2025-10-08 15:47:36] group size being clustered:  67,14007
INFO [2025-10-08 15:47:36] Number of cells in group(7) is 4
INFO [2025-10-08 15:47:36] group size being clustered:  4,14007
INFO [2025-10-08 15:47:36] Number of cells in group(8) is 70
INFO [2025-10-08 15:47:36] group size being clustered:  70,14007
INFO [2025-10-08 15:47:36] Number of cells in group(9) is 21
INFO [2025-10-08 15:47:36] group size being clustered:  21,14007
INFO [2025-10-08 15:47:36] Number of cells in group(10) is 2
INFO [2025-10-08 15:47:36] group size being clustered:  2,14007
INFO [2025-10-08 15:47:36] Number of cells in group(11) is 43
INFO [2025-10-08 15:47:36] group size being clustered:  43,14007
INFO [2025-10-08 15:47:36] Number of cells in group(12) is 72
INFO [2025-10-08 15:47:36] group size being clustered:  72,14007
INFO [2025-10-08 15:47:36] Number of cells in group(13) is 14
INFO [2025-10-08 15:47:36] group size being clustered:  14,14007
INFO [2025-10-08 15:47:36] Number of cells in group(14) is 58
INFO [2025-10-08 15:47:36] group size being clustered:  58,14007
INFO [2025-10-08 15:47:36] Number of cells in group(15) is 8
INFO [2025-10-08 15:47:36] group size being clustered:  8,14007
INFO [2025-10-08 15:47:36] plot_cnv_observation:Writing observation groupings/color.
INFO [2025-10-08 15:47:36] plot_cnv_observation:Done writing observation groupings/color.
INFO [2025-10-08 15:47:36] plot_cnv_observation:Writing observation heatmap thresholds.
INFO [2025-10-08 15:47:36] plot_cnv_observation:Done writing observation heatmap thresholds.
INFO [2025-10-08 15:47:37] Colors for breaks:  #00008B,#24249B,#4848AB,#6D6DBC,#9191CC,#B6B6DD,#DADAEE,#FFFFFF,#EEDADA,#DDB6B6,#CC9191,#BC6D6D,#AB4848,#9B2424,#8B0000
INFO [2025-10-08 15:47:37] Quantiles of plotted data range: 0.967125515789634,0.998062798164108,0.99929349444528,1.00072939080162,1.03340940953818
INFO [2025-10-08 15:47:37] plot_cnv_references:Start
INFO [2025-10-08 15:47:37] Reference data size: Cells= 77 Genes= 14007
INFO [2025-10-08 15:47:37] plot_cnv_references:Number reference groups= 3
INFO [2025-10-08 15:47:37] plot_cnv_references:Plotting heatmap.
INFO [2025-10-08 15:47:37] Colors for breaks:  #00008B,#24249B,#4848AB,#6D6DBC,#9191CC,#B6B6DD,#DADAEE,#FFFFFF,#EEDADA,#DDB6B6,#CC9191,#BC6D6D,#AB4848,#9B2424,#8B0000
INFO [2025-10-08 15:47:37] Quantiles of plotted data range: 0.967125515789634,0.998069252504531,0.999297520712723,1.00074408335968,1.03340940953818
INFO [2025-10-08 15:47:45] ::plot_cnv:Start
INFO [2025-10-08 15:47:45] ::plot_cnv:Current data dimensions (r,c)=14007,575 Total=8056179.15098159 Min=0.827673338207976 Max=1.15777732284435.
INFO [2025-10-08 15:47:45] ::plot_cnv:Depending on the size of the matrix this may take a moment.
INFO [2025-10-08 15:47:45] plot_cnv(): auto thresholding at: (0.967126 , 1.033409)
INFO [2025-10-08 15:47:45] plot_cnv_observation:Start
INFO [2025-10-08 15:47:45] Observation data size: Cells= 498 Genes= 14007
INFO [2025-10-08 15:47:45] plot_cnv_observation:Writing observation groupings/color.
INFO [2025-10-08 15:47:45] plot_cnv_observation:Done writing observation groupings/color.
INFO [2025-10-08 15:47:45] plot_cnv_observation:Writing observation heatmap thresholds.
INFO [2025-10-08 15:47:45] plot_cnv_observation:Done writing observation heatmap thresholds.
INFO [2025-10-08 15:47:46] Colors for breaks:  #00008B,#24249B,#4848AB,#6D6DBC,#9191CC,#B6B6DD,#DADAEE,#FFFFFF,#EEDADA,#DDB6B6,#CC9191,#BC6D6D,#AB4848,#9B2424,#8B0000
INFO [2025-10-08 15:47:46] Quantiles of plotted data range: 0.967125515789634,0.998062798164108,0.99929349444528,1.00072939080162,1.03340940953818
INFO [2025-10-08 15:47:46] plot_cnv_references:Start
INFO [2025-10-08 15:47:46] Reference data size: Cells= 77 Genes= 14007
INFO [2025-10-08 15:47:46] plot_cnv_references:Number reference groups= 1
INFO [2025-10-08 15:47:46] plot_cnv_references:Plotting heatmap.
INFO [2025-10-08 15:47:46] Colors for breaks:  #00008B,#24249B,#4848AB,#6D6DBC,#9191CC,#B6B6DD,#DADAEE,#FFFFFF,#EEDADA,#DDB6B6,#CC9191,#BC6D6D,#AB4848,#9B2424,#8B0000
INFO [2025-10-08 15:47:46] Quantiles of plotted data range: 0.967125515789634,0.998069252504531,0.999297520712723,1.00074408335968,1.03340940953818
INFO [2025-10-08 15:47:47] 

	STEP 17: HMM-based CNV prediction

INFO [2025-10-08 15:47:47] i3HMM_predict_CNV_via_HMM_on_tumor_subclusters(i3_p_val=0.05, use_KS=FALSE)
INFO [2025-10-08 15:47:47] determine mean delta (sigma: 0.00875461, p=0.05) -> 0.0144
INFO [2025-10-08 15:47:51] -done predicting CNV based on initial tumor subclusters
INFO [2025-10-08 15:47:51] get_predicted_CNV_regions(subcluster)
INFO [2025-10-08 15:47:51] -processing cell_group_name: Advanced Mesoderm.Advanced Mesoderm_s1, size: 47
INFO [2025-10-08 15:47:56] -processing cell_group_name: Advanced Mesoderm.Advanced Mesoderm_s2, size: 1
INFO [2025-10-08 15:48:01] -processing cell_group_name: Axial Mesoderm.Axial Mesoderm_s1, size: 37
INFO [2025-10-08 15:48:06] -processing cell_group_name: Emergent Mesoderm.Emergent Mesoderm, size: 9
INFO [2025-10-08 15:48:10] -processing cell_group_name: Endoderm.Endoderm_s1, size: 45
INFO [2025-10-08 15:48:15] -processing cell_group_name: Erythrocytes.Erythrocytes_s1, size: 67
INFO [2025-10-08 15:48:20] -processing cell_group_name: Erythrocytes.Erythrocytes_s2, size: 4
INFO [2025-10-08 15:48:25] -processing cell_group_name: Extra-embryonic Mesoderm.Extra-embryonic Mesoderm_s1, size: 70
INFO [2025-10-08 15:48:30] -processing cell_group_name: Extra-embryonic Mesoderm.Extra-embryonic Mesoderm_s2, size: 21
INFO [2025-10-08 15:48:34] -processing cell_group_name: Extra-embryonic Mesoderm.Extra-embryonic Mesoderm_s3, size: 2
INFO [2025-10-08 15:48:39] -processing cell_group_name: HEP.HEP_s1, size: 43
INFO [2025-10-08 15:48:44] -processing cell_group_name: Nascent Mesoderm.Nascent Mesoderm_s1, size: 72
INFO [2025-10-08 15:48:49] -processing cell_group_name: Nascent Mesoderm.Nascent Mesoderm_s2, size: 14
INFO [2025-10-08 15:48:54] -processing cell_group_name: Primative Streak.Primative Streak_s1, size: 58
INFO [2025-10-08 15:49:00] -processing cell_group_name: Primative Streak.Primative Streak_s2, size: 8
INFO [2025-10-08 15:49:04] -processing cell_group_name: Epiblast.Epiblast_s1, size: 44
INFO [2025-10-08 15:49:09] -processing cell_group_name: Epiblast.Epiblast_s3, size: 19
INFO [2025-10-08 15:49:14] -processing cell_group_name: Epiblast.Epiblast_s2, size: 14
INFO [2025-10-08 15:49:19] -writing cell clusters file: output_dir/17_HMM_predHMMi3.leiden.hmm_mode-subclusters.cell_groupings
INFO [2025-10-08 15:49:19] -writing cnv regions file: output_dir/17_HMM_predHMMi3.leiden.hmm_mode-subclusters.pred_cnv_regions.dat
INFO [2025-10-08 15:49:19] -writing per-gene cnv report: output_dir/17_HMM_predHMMi3.leiden.hmm_mode-subclusters.pred_cnv_genes.dat
INFO [2025-10-08 15:49:19] -writing gene ordering info: output_dir/17_HMM_predHMMi3.leiden.hmm_mode-subclusters.genes_used.dat
INFO [2025-10-08 15:49:23] ::plot_cnv:Start
INFO [2025-10-08 15:49:23] ::plot_cnv:Current data dimensions (r,c)=14007,575 Total=16053252 Min=1 Max=3.
INFO [2025-10-08 15:49:23] ::plot_cnv:Depending on the size of the matrix this may take a moment.
INFO [2025-10-08 15:49:23] plot_cnv_observation:Start
INFO [2025-10-08 15:49:23] Observation data size: Cells= 498 Genes= 14007
INFO [2025-10-08 15:49:23] plot_cnv_observation:Writing observation groupings/color.
INFO [2025-10-08 15:49:23] plot_cnv_observation:Done writing observation groupings/color.
INFO [2025-10-08 15:49:23] plot_cnv_observation:Writing observation heatmap thresholds.
INFO [2025-10-08 15:49:23] plot_cnv_observation:Done writing observation heatmap thresholds.
INFO [2025-10-08 15:49:24] Colors for breaks:  #00008B,#24249B,#4848AB,#6D6DBC,#9191CC,#B6B6DD,#DADAEE,#FFFFFF,#EEDADA,#DDB6B6,#CC9191,#BC6D6D,#AB4848,#9B2424,#8B0000
INFO [2025-10-08 15:49:24] Quantiles of plotted data range: 1,2,2,2,3
INFO [2025-10-08 15:49:24] plot_cnv_references:Start
INFO [2025-10-08 15:49:24] Reference data size: Cells= 77 Genes= 14007
INFO [2025-10-08 15:49:24] plot_cnv_references:Number reference groups= 1
INFO [2025-10-08 15:49:24] plot_cnv_references:Plotting heatmap.
INFO [2025-10-08 15:49:24] Colors for breaks:  #00008B,#24249B,#4848AB,#6D6DBC,#9191CC,#B6B6DD,#DADAEE,#FFFFFF,#EEDADA,#DDB6B6,#CC9191,#BC6D6D,#AB4848,#9B2424,#8B0000
INFO [2025-10-08 15:49:24] Quantiles of plotted data range: 1,2,2,2,3
INFO [2025-10-08 15:49:25] 

	STEP 18: Run Bayesian Network Model on HMM predicted CNVs

INFO [2025-10-08 15:49:25] Creating the following Directory:  output_dir/BayesNetOutput.HMMi3.leiden.hmm_mode-subclusters
INFO [2025-10-08 15:49:25] Initializing new MCM InferCNV Object.
INFO [2025-10-08 15:49:25] validating infercnv_obj
INFO [2025-10-08 15:49:25] Total CNV's:  215
INFO [2025-10-08 15:49:25] Loading BUGS Model.
INFO [2025-10-08 15:49:25] Running Sampling Using Parallel with  4 Cores
INFO [2025-10-08 15:49:33] Obtaining probabilities post-sampling
INFO [2025-10-08 15:49:35] Gibbs sampling time:  0.164916165669759  Minutes
INFO [2025-10-08 15:49:44] ::plot_cnv:Start
INFO [2025-10-08 15:49:44] ::plot_cnv:Current data dimensions (r,c)=14007,575 Total=81272.6424277677 Min=0 Max=0.941422250860327.
INFO [2025-10-08 15:49:44] ::plot_cnv:Depending on the size of the matrix this may take a moment.
INFO [2025-10-08 15:49:44] plot_cnv_observation:Start
INFO [2025-10-08 15:49:44] Observation data size: Cells= 498 Genes= 14007
INFO [2025-10-08 15:49:44] plot_cnv_observation:Writing observation groupings/color.
INFO [2025-10-08 15:49:44] plot_cnv_observation:Done writing observation groupings/color.
INFO [2025-10-08 15:49:44] plot_cnv_observation:Writing observation heatmap thresholds.
INFO [2025-10-08 15:49:44] plot_cnv_observation:Done writing observation heatmap thresholds.
INFO [2025-10-08 15:49:45] Colors for breaks:  #00008B,#24249B,#4848AB,#6D6DBC,#9191CC,#B6B6DD,#DADAEE,#FFFFFF,#EEDADA,#DDB6B6,#CC9191,#BC6D6D,#AB4848,#9B2424,#8B0000
INFO [2025-10-08 15:49:45] Quantiles of plotted data range: 0,0,0,0,0.910900693025275
INFO [2025-10-08 15:49:45] plot_cnv_references:Start
INFO [2025-10-08 15:49:45] Reference data size: Cells= 77 Genes= 14007
INFO [2025-10-08 15:49:45] plot_cnv_references:Number reference groups= 1
INFO [2025-10-08 15:49:45] plot_cnv_references:Plotting heatmap.
INFO [2025-10-08 15:49:45] Colors for breaks:  #00008B,#24249B,#4848AB,#6D6DBC,#9191CC,#B6B6DD,#DADAEE,#FFFFFF,#EEDADA,#DDB6B6,#CC9191,#BC6D6D,#AB4848,#9B2424,#8B0000
INFO [2025-10-08 15:49:45] Quantiles of plotted data range: 0,0,0,0,0.941422250860327
INFO [2025-10-08 15:49:53] 

	STEP 19: Filter HMM predicted CNVs based on the Bayesian Network Model results and BayesMaxPNormal

INFO [2025-10-08 15:49:53] Attempting to removing CNV(s) with a probability of being normal above  0.5
INFO [2025-10-08 15:49:53] Removing  35  CNV(s) identified by the HMM.
INFO [2025-10-08 15:49:53] Total CNV's after removing:  180
INFO [2025-10-08 15:49:53] Reassigning CNVs based on state probabilities.
INFO [2025-10-08 15:49:53] Changing the following CNV's states assigned by the HMM to the following based on the CNV's state probabilities.
 chr3-region_244 : 1  (P= 0.427350981725549 ) ->  2 (P= 0.427648153209705 )
chr19-region_290 : 3  (P= 0.425837054407641 ) ->  2 (P= 0.429151149292175 )
chr6-region_335 : 1  (P= 0.3814386660175 ) ->  2 (P= 0.493360696737336 )
chr1-region_368 : 3  (P= 0.399579349466875 ) ->  2 (P= 0.401416806826951 )
chr3-region_372 : 3  (P= 0.399579349466875 ) ->  2 (P= 0.401416806826951 )
chr3-region_374 : 3  (P= 0.399579349466875 ) ->  2 (P= 0.401416806826951 )
chr11-region_400 : 3  (P= 0.394019122174418 ) ->  2 (P= 0.402868665981718 )
chr16-region_408 : 3  (P= 0.394019122174418 ) ->  2 (P= 0.402868665981718 )
chr16-region_410 : 3  (P= 0.394019122174418 ) ->  2 (P= 0.402868665981718 )
chr17-region_495 : 1  (P= 0.47082392211849 ) ->  2 (P= 0.472716392363994 )
chr2-region_662 : 3  (P= 0.452343005283811 ) ->  2 (P= 0.488833560093813 )
INFO [2025-10-08 15:49:53] Creating Plots for CNV and cell Probabilities.
INFO [2025-10-08 15:50:24] ::plot_cnv:Start
INFO [2025-10-08 15:50:24] ::plot_cnv:Current data dimensions (r,c)=14007,575 Total=66359.6676657521 Min=0 Max=0.941422250860327.
INFO [2025-10-08 15:50:24] ::plot_cnv:Depending on the size of the matrix this may take a moment.
INFO [2025-10-08 15:50:24] plot_cnv_observation:Start
INFO [2025-10-08 15:50:24] Observation data size: Cells= 498 Genes= 14007
INFO [2025-10-08 15:50:24] plot_cnv_observation:Writing observation groupings/color.
INFO [2025-10-08 15:50:24] plot_cnv_observation:Done writing observation groupings/color.
INFO [2025-10-08 15:50:24] plot_cnv_observation:Writing observation heatmap thresholds.
INFO [2025-10-08 15:50:24] plot_cnv_observation:Done writing observation heatmap thresholds.
INFO [2025-10-08 15:50:25] Colors for breaks:  #00008B,#24249B,#4848AB,#6D6DBC,#9191CC,#B6B6DD,#DADAEE,#FFFFFF,#EEDADA,#DDB6B6,#CC9191,#BC6D6D,#AB4848,#9B2424,#8B0000
INFO [2025-10-08 15:50:25] Quantiles of plotted data range: 0,0,0,0,0.910900693025275
INFO [2025-10-08 15:50:25] plot_cnv_references:Start
INFO [2025-10-08 15:50:25] Reference data size: Cells= 77 Genes= 14007
INFO [2025-10-08 15:50:25] plot_cnv_references:Number reference groups= 1
INFO [2025-10-08 15:50:25] plot_cnv_references:Plotting heatmap.
INFO [2025-10-08 15:50:25] Colors for breaks:  #00008B,#24249B,#4848AB,#6D6DBC,#9191CC,#B6B6DD,#DADAEE,#FFFFFF,#EEDADA,#DDB6B6,#CC9191,#BC6D6D,#AB4848,#9B2424,#8B0000
INFO [2025-10-08 15:50:25] Quantiles of plotted data range: 0,0,0,0,0.941422250860327
INFO [2025-10-08 15:50:28] ::plot_cnv:Start
INFO [2025-10-08 15:50:28] ::plot_cnv:Current data dimensions (r,c)=14007,575 Total=16046257 Min=1 Max=3.
INFO [2025-10-08 15:50:29] ::plot_cnv:Depending on the size of the matrix this may take a moment.
INFO [2025-10-08 15:50:29] plot_cnv_observation:Start
INFO [2025-10-08 15:50:29] Observation data size: Cells= 498 Genes= 14007
INFO [2025-10-08 15:50:29] plot_cnv_observation:Writing observation groupings/color.
INFO [2025-10-08 15:50:29] plot_cnv_observation:Done writing observation groupings/color.
INFO [2025-10-08 15:50:29] plot_cnv_observation:Writing observation heatmap thresholds.
INFO [2025-10-08 15:50:29] plot_cnv_observation:Done writing observation heatmap thresholds.
INFO [2025-10-08 15:50:29] Colors for breaks:  #00008B,#24249B,#4848AB,#6D6DBC,#9191CC,#B6B6DD,#DADAEE,#FFFFFF,#EEDADA,#DDB6B6,#CC9191,#BC6D6D,#AB4848,#9B2424,#8B0000
INFO [2025-10-08 15:50:29] Quantiles of plotted data range: 1,2,2,2,3
INFO [2025-10-08 15:50:30] plot_cnv_references:Start
INFO [2025-10-08 15:50:30] Reference data size: Cells= 77 Genes= 14007
INFO [2025-10-08 15:50:30] plot_cnv_references:Number reference groups= 1
INFO [2025-10-08 15:50:30] plot_cnv_references:Plotting heatmap.
INFO [2025-10-08 15:50:30] Colors for breaks:  #00008B,#24249B,#4848AB,#6D6DBC,#9191CC,#B6B6DD,#DADAEE,#FFFFFF,#EEDADA,#DDB6B6,#CC9191,#BC6D6D,#AB4848,#9B2424,#8B0000
INFO [2025-10-08 15:50:30] Quantiles of plotted data range: 1,2,2,2,3
INFO [2025-10-08 15:50:30] 

	STEP 20: Converting HMM-based CNV states to repr expr vals

INFO [2025-10-08 15:50:33] ::plot_cnv:Start
INFO [2025-10-08 15:50:33] ::plot_cnv:Current data dimensions (r,c)=14007,575 Total=8023128.5 Min=0.5 Max=1.5.
INFO [2025-10-08 15:50:33] ::plot_cnv:Depending on the size of the matrix this may take a moment.
INFO [2025-10-08 15:50:33] plot_cnv_observation:Start
INFO [2025-10-08 15:50:33] Observation data size: Cells= 498 Genes= 14007
INFO [2025-10-08 15:50:33] plot_cnv_observation:Writing observation groupings/color.
INFO [2025-10-08 15:50:33] plot_cnv_observation:Done writing observation groupings/color.
INFO [2025-10-08 15:50:33] plot_cnv_observation:Writing observation heatmap thresholds.
INFO [2025-10-08 15:50:33] plot_cnv_observation:Done writing observation heatmap thresholds.
INFO [2025-10-08 15:50:34] Colors for breaks:  #00008B,#24249B,#4848AB,#6D6DBC,#9191CC,#B6B6DD,#DADAEE,#FFFFFF,#EEDADA,#DDB6B6,#CC9191,#BC6D6D,#AB4848,#9B2424,#8B0000
INFO [2025-10-08 15:50:34] Quantiles of plotted data range: 0.5,1,1,1,1.5
INFO [2025-10-08 15:50:34] plot_cnv_references:Start
INFO [2025-10-08 15:50:34] Reference data size: Cells= 77 Genes= 14007
INFO [2025-10-08 15:50:35] plot_cnv_references:Number reference groups= 1
INFO [2025-10-08 15:50:35] plot_cnv_references:Plotting heatmap.
INFO [2025-10-08 15:50:35] Colors for breaks:  #00008B,#24249B,#4848AB,#6D6DBC,#9191CC,#B6B6DD,#DADAEE,#FFFFFF,#EEDADA,#DDB6B6,#CC9191,#BC6D6D,#AB4848,#9B2424,#8B0000
INFO [2025-10-08 15:50:35] Quantiles of plotted data range: 0.5,1,1,1,1.5
INFO [2025-10-08 15:50:35] 

	STEP 22: Denoising

INFO [2025-10-08 15:50:35] ::process_data:Remove noise, noise threshold defined via ref mean sd_amplifier:  1.5
INFO [2025-10-08 15:50:35] denoising using mean(normal) +- sd_amplifier * sd(normal) per gene per cell across all data
INFO [2025-10-08 15:50:35] :: **** clear_noise_via_ref_quantiles **** : removing noise between bounds:  0.989885865291573 - 1.01019101265148
INFO [2025-10-08 15:50:40] 

## Making the final infercnv heatmap ##
INFO [2025-10-08 15:50:41] ::plot_cnv:Start
INFO [2025-10-08 15:50:41] ::plot_cnv:Current data dimensions (r,c)=14007,575 Total=8060977.94547191 Min=0.827673338207976 Max=1.15777732284435.
INFO [2025-10-08 15:50:41] ::plot_cnv:Depending on the size of the matrix this may take a moment.
INFO [2025-10-08 15:50:41] plot_cnv(): auto thresholding at: (0.966591 , 1.033409)
INFO [2025-10-08 15:50:41] plot_cnv_observation:Start
INFO [2025-10-08 15:50:41] Observation data size: Cells= 498 Genes= 14007
INFO [2025-10-08 15:50:41] plot_cnv_observation:Writing observation groupings/color.
INFO [2025-10-08 15:50:41] plot_cnv_observation:Done writing observation groupings/color.
INFO [2025-10-08 15:50:41] plot_cnv_observation:Writing observation heatmap thresholds.
INFO [2025-10-08 15:50:41] plot_cnv_observation:Done writing observation heatmap thresholds.
INFO [2025-10-08 15:50:42] Colors for breaks:  #00008B,#24249B,#4848AB,#6D6DBC,#9191CC,#B6B6DD,#DADAEE,#FFFFFF,#EEDADA,#DDB6B6,#CC9191,#BC6D6D,#AB4848,#9B2424,#8B0000
INFO [2025-10-08 15:50:42] Quantiles of plotted data range: 0.96659059046182,1.00003843897153,1.00003843897153,1.00003843897153,1.03340940953818
INFO [2025-10-08 15:50:42] plot_cnv_references:Start
INFO [2025-10-08 15:50:42] Reference data size: Cells= 77 Genes= 14007
INFO [2025-10-08 15:50:42] plot_cnv_references:Number reference groups= 1
INFO [2025-10-08 15:50:42] plot_cnv_references:Plotting heatmap.
INFO [2025-10-08 15:50:42] Colors for breaks:  #00008B,#24249B,#4848AB,#6D6DBC,#9191CC,#B6B6DD,#DADAEE,#FFFFFF,#EEDADA,#DDB6B6,#CC9191,#BC6D6D,#AB4848,#9B2424,#8B0000
INFO [2025-10-08 15:50:42] Quantiles of plotted data range: 0.96659059046182,1.00003843897153,1.00003843897153,1.00003843897153,1.03340940953818
Warning: Data is of class matrix. Coercing to dgCMatrix.
Finding variable features for layer counts
Calculating gene variances
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Centering and scaling data matrix

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PC_ 1 
Positive:  CRELD2, ALG12, FTLP3, SMOX, PRNP, SLC1A6, OR7C1, RASSF2, ZNF333, SLC23A2 
	   NDUFB7, TECR, TMEM230, DNAJB1, GIPC1, PCNA, PTGER1, CDS2, ATPAF2, LINC00654 
	   PKN1, TOM1L2, GPCPD1, DDX39A, CHGB, TRMT6, SREBF1, MCM8, ASF1B, CRLS1 
Negative:  IL6ST, ITGA2, NNT, SPRTN, ARV1, PTK2B, BNIP3L, SDAD1P1, AC022431.1, MIR5687 
	   GPX8, ARL15, PELO, ITGA1, ISL1, RBMS1, PLA2R1, LY75, CD302, BAZ2B 
	   KCNK1, NTPCR, GNPAT, EPHX2, TRIM35, CDCA2, SETD9, MAP3K1, ANKRD55, FST 
PC_ 2 
Positive:  GIT2, ANKRD13A, TCHP, C12orf76, GLTP, RBFOX2, APOL6, MB, FAM216A, RASD2 
	   TCTN1, MCM5, HVCN1, PPP1CC, HMOX1, CCDC63, CUX2, TOM1, SH2B3, HMGXB4 
	   PDE10A, NAA25, TRAFD1, HECTD4, PTPN11, RPH3A, C6orf118, TIMP3, OAS1, APPBP2 
Negative:  SPPL2B, OAZ1, SF3A2, WASH5P, LINC01002, PLEKHJ1, MIER2, DOT1L, SHC2, MADCAM1 
	   AP3D1, TPGS1, CDC34, MOB3A, BSG, POLRMT, MKNK2, RNF126, FSTL3, BTBD2 
	   PRSS57, PTBP1, CSNK1G2, AZU1, AC012615.1, CFD, MED16, ADAT3, R3HDM4, KISS1R 
PC_ 3 
Positive:  AL080243.1, EP300, L3MBTL2, RANGAP1, ZC3H7B, DNAJB7, TEF, TOB2, PHF5A, ACO2 
	   POLR3H, PMM1, DESI1, XRCC6, C22orf46, MEI1, CCDC134, SREBF2, KPTN, NAPA 
	   MEIS3, TNFRSF13C, PLA2G4C, DHX34, LIG1, C5AR1, CCDC9, CENPM, SAE1, SYNGR2 
Negative:  RTN4RL2, SLC43A1, SLC43A3, TIMM10, PRG2, SMTNL1, P2RX3, SSRP1, UBE2L6, TNKS1BP1 
	   SERPING1, APLNR, CLP1, OR5M2P, ZDHHC5, FOLH1, MED19, PTPRJ, TMX2, CTNND1 
	   LPXN, ZFP91, GLYATL2, AP001652.1, FAM111B, MGLL, SEC61A1, ABTB1, RUVBL1, PODXL2 
PC_ 4 
Positive:  CRTC3, BLM, FES, MAN2A2, UNC45A, HDDC3, RCCD1, ANKRD13A, C12orf76, PRC1 
	   GIT2, PEX11A, TCHP, KIF7, VPS33B, TICRR, GLTP, POLG, FANCI, SLCO3A1 
	   ABHD2, FAM216A, MFGE8, HAPLN3, TCTN1, ST8SIA2, ACAN, HVCN1, ISG20, PPP1CC 
Negative:  MGLL, ABTB1, PODXL2, SEC61A1, MCM2, RUVBL1, TPRA1, EEFSEC, PLXNA1, GATA2 
	   RPN1, CHCHD6, RAB7A, ACAD9, TXNRD3, KIAA1257, EFCC1, GP9, RAB43, ISY1 
	   CNBP, COPG1, HMCES, H1FX, RPL32P3, EFCAB12, MBD4, IFT122, PLXND1, TMCC1 
PC_ 5 
Positive:  HIGD1A, CCDC13, ACKR2, KRBOX1, ZNF662, FAM198A, POMGNT2, SNRK, ANO10, ABHD5 
	   TCAIM, ZNF445, ZNF852, ZKSCAN7, ZNF660, MPRIPP1, ZNF197, PDE10A, ZNF35, APPBP2 
	   C6orf118, ZNF502, QKI, ZNF501, PPM1D, CAHM, BCAS3, KIAA1143, BRIP1, PACRG 
Negative:  DHDDS, LIN28A, CD52, UBXN11, SH3BGRL3, CEP85, CNKSR1, ZNF593, HMGN2, FAM110D 
	   PDIK1L, AL391650.1, EXTL1, SCARNA18, PAFAH2, STMN1, PAQR7, AUNIP, RPS6KA1, AL020996.1 
	   MTFR1L, MAN1C1, ARID1A, TEX10, MSANTD3, LDLRAP1, INVS, ERP44, TMEFF1, STX17 
Computing nearest neighbor graph
Computing SNN
Warning: Data is of class matrix. Coercing to dgCMatrix.
Finding variable features for layer counts
Calculating gene variances
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Centering and scaling data matrix

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PC_ 1 
Positive:  MYOM1, ERGIC2, AC009299.2, PLK2, PKP4, AGT, GPBP1, TANC1, CAPN9, MIER3 
	   WDSUB1, P2RX4, C1orf198, WDR66, CAMKK2, KANSL2, PSMD9, ASB8, RBPMS, ERLIN2 
	   ACAD8, FKBP4, ALG10, PCED1B, PFKM, ROCK1P1, TTC13, EGLN1, KCNK1, AC009506.1 
Negative:  PAQR8, EFHC1, MCM3, TRAM2, FTH1P5, TFAP2B, TMEM14A, RHAG, ZBTB43, ZBTB34 
	   PPIL1, RALGPS1, C6orf141, GSTA2, ANGPTL2, GARNL3, SLC2A8, ZNF79, CENPQ, LRSAM1 
	   STXBP1, PCMTD2, PTRH1, TTC16, MUT, TOR2A, SH2D3C, CDK9, FPGS, ENG 
PC_ 2 
Positive:  PGPEP1L, TTC23, LRRC28, IGF1R, KIF7, PEX11A, AC022819.1, TICRR, ARRDC4, POLG 
	   FES, BLM, MAN2A2, CRTC3, UNC45A, HDDC3, MEF2A, RCCD1, PRC1, LINC00923 
	   VPS33B, SLCO3A1, LYSMD4, ST8SIA2, MCTP2, FAM174B, CHD2, AC090825.1, RGMA, ADAMTS17 
Negative:  YIPF4, BIRC6, NLRC4, TTC27, SLC30A6, LTBP1, SPAST, RASGRP3, MEMO1, FAM98A 
	   AC073218.2, ZNF852, ZKSCAN7, ZNF445, ZNF660, TCAIM, MPRIPP1, ZNF197, ZNF35, CRIM1 
	   ABHD5, KIAA1143, ZNF502, ZNF501, ANO10, FEZ2, SNRK, VIT, STRN, POMGNT2 
PC_ 3 
Positive:  SCAND2P, ZSCAN2, LINC00933, EGLN1P1, WDR73, NMB, SEC11A, ALPK3, AC044860.1, AKAP13 
	   KLHL25, NTRK3, MRPL46, MRPS11, DET1, AC013489.1, AEN, ISG20, ACAN, HAPLN3 
	   MFGE8, ABHD2, FANCI, PGPEP1L, TTC23, IGF1R, LRRC28, CRTC3, BLM, FES 
Negative:  TMEM219, TAOK2, KCTD13, HIRIP3, ASPHD1, SEZ6L2, INO80E, CDIPT, DOC2A, MVP 
	   PAGR1, ALDOA, PRRT2, PPP4C, MAZ, KIF22, TBX6, ZG16, QPRT, SPN 
	   YPEL3, SLC7A5P1, SULT1A4, SLX1B, MAPK3, NPIPB11, ABTB1, MGLL, SNX29P2, PODXL2 
PC_ 4 
Positive:  RPS15AP10, TMEM69, GPBP1L1, IPP, MAST2, PIK3R3, TSPAN1, POMGNT1, LURAP1, RAD54L 
	   LRRC41, NSUN4, FAAH, DMBX1, MKNK1, MOB3C, ATPAF1, EFCAB14, CYP4B1, CYP4A11 
	   CYP4X1, CYP4Z1, LINC00853, PDZK1IP1, TAL1, STIL, CMPK1, TRABD2B, SLC5A9, SPATA6 
Negative:  PREPL, CAMKMT, SRSF7, SIX3, SRBD1, PRKCE, EPAS1, ATP6V1E2, RHOQ, PIGF 
	   GALM, CRIPT, SOCS5, AC016722.1, HNRNPLL, AC016722.2, MCFD2, RPLP0P6, TTC7A, ATL2 
	   EPCAM, CYP1B1, MSH2, RMDN2, KCNK12, CDC42EP3, AC079250.1, QPCT, MSH6, PRKD3 
PC_ 5 
Positive:  TBX15, WARS2, SPAG17, PHGDH, HMGCS2, WDR3, NOTCH2, GDAP2, FAM72B, HIST2H3DP1 
	   MAN1A2, HIST2H2BA, TRIM45, SEC22B, PDE4DIP, NBPF10, NBPF9, PFN1P2, NBPF8, SRGAP2B 
	   FAM72D, EMBP1, SRGAP2C, FCGR1B, TTF2, PTGFRN, AP4B1, PTPN22, DCLRE1B, HIPK1 
Negative:  GADD45GIP1, RAD23A, CALR, DAND5, FARSA, LYL1, TRMT1, SYCE2, NACC1, GCDH 
	   XYLB, OXSR1, STX10, MYD88, IER2, ACAA1, KLF1, CACNA1A, DLEC1, CCDC130 
	   PLCD1, MRI1, BAIAP2L2, DNASE2, ZSWIM4, PALM3, C19orf67, IL27RA, SAMD1, PRKACA 
Computing nearest neighbor graph
Computing SNN
Warning: Data is of class matrix. Coercing to dgCMatrix.
Finding variable features for layer counts
Calculating gene variances
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Centering and scaling data matrix

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PC_ 1 
Positive:  PGBD5, GALNT2, SECTM1, URB2, PART1, KANSL3, C12orf56, CD7, NUFIP1, TAF5L 
	   DEPDC1B, XPOT, LMAN2L, LINC00330, PLA2G16, RND1, ABCB10, CSNK1D, AIMP1, ELOVL7 
	   TSC22D1, TBK1, LGALS12, CNNM4, PAPSS1, NUP133, SERP2, GNL3LP1, CCDC65, HRASLS5 
Negative:  CTCF, ATP6V0D1, HSD11B2, ZDHHC1, TPPP3, LRRC36, PLEKHG4, SLC9A5, FHOD1, TMEM208 
	   LRRC29, MRAS, NME9, C2CD4D, TDRKH, ARMC8, OAZ3, RAB13, MRPL9, RIIAD1 
	   SNX27, DBR1, TUFT1, CGN, POGZ, DZIP1L, NCK1, FCGR1B, HIST2H2BA, HIST2H3DP1 
PC_ 2 
Positive:  AIDA, MIA3, DISP1, TAF1A, NDUFB1P2, SNX24, PPIC, SNX2, SUSD4, AC106786.1 
	   HHIPL2, SNCAIP, ZNF474, CAPN2, CEP120, DUSP10, LOX, TP53BP2, CSNK1G3, SRFBP1 
	   HLX, AC138393.1, PRR16, ZNF608, FBXO28, MARC1, HSD17B4, DEGS1, ALDH7A1, MARC2 
Negative:  TXNRD3, CHCHD6, PLXNA1, TPRA1, MCM2, PODXL2, ABTB1, MGLL, SEC61A1, RUVBL1 
	   EEFSEC, GATA2, RPN1, RAB7A, ACAD9, KIAA1257, EFCC1, GP9, RAB43, ISY1 
	   CNBP, COPG1, HMCES, H1FX, RPL32P3, EFCAB12, MBD4, IFT122, PLXND1, TMCC1 
PC_ 3 
Positive:  GALR3, ANKRD54, EIF3L, MICALL1, GCAT, C22orf23, H1F0, POLR2F, PICK1, TRIOBP 
	   SLC16A8, BAIAP2L2, NOL12, PLA2G6, PDXP, MAFF, TMEM184B, Z83844.1, CSNK1E, SH3BP1 
	   KDELR3, DDX17, GGA1, DMC1, FAM227A, LGALS2, SLCO3A1, VPS33B, ST8SIA2, PRC1 
Negative:  RTBDN, RNASEH2A, MAST1, PRDX2, AC020934.1, JUNB, DNASE2, HOOK2, KLF1, GCDH 
	   ASNA1, SYCE2, TNPO2, FARSA, FBXW9, CALR, RAD23A, DHPS, GADD45GIP1, DAND5 
	   WDR83, LYL1, TRMT1, MAN2B1, NACC1, STX10, ZNF791, IER2, CACNA1A, ZNF490 
PC_ 4 
Positive:  NPTX1, ENDOV, RPTOR, RNF213, CHMP6, SLC26A11, BAIAP2, SGSH, EIF4A3, AATK 
	   SLC38A10, ACTG1, NPLOC4, PDE6G, OXLD1, GAA, CCDC137, AC139530.1, ARL16, CCDC40 
	   HGS, MRPL12, TBC1D16, SLC25A10, GCGR, CBX4, PPP1R27, CBX8, P4HB, ARHGDIA 
Negative:  NDUFB1P2, DISP1, AIDA, SUSD4, MIA3, CAPN2, TAF1A, TP53BP2, HHIPL2, AC138393.1 
	   DUSP10, FBXO28, HLX, DEGS1, MARC1, NVL, MARC2, DNAH14, C1orf115, LBR 
	   ENAH, MARK1, EPHX1, RAB3GAP2, TMEM63A, IARS2, LEFTY1, BPNT1, PYCR2, EPRS 
PC_ 5 
Positive:  FAM117A, SLC35B1, KAT7, SPOP, TAC4, NGFR, PHB, DLX4, ZNF652, DLX3 
	   ABI3, GNGT2, ITGA3, IGF2BP1, PDK2, SNF8, UBE2Z, PPP1R9B, CALCOCO2, TTLL6 
	   HOXB13, SGCA, HOXB9, HOXB8, HOXB7, HOXB6, HOXB5, STPG1, NIPAL3, GRHL3 
Negative:  C22orf23, MICALL1, POLR2F, EIF3L, PICK1, ANKRD54, SLC16A8, GALR3, BAIAP2L2, PLA2G6 
	   GCAT, MAFF, TMEM184B, H1F0, CSNK1E, KDELR3, TRIOBP, DDX17, DMC1, NOL12 
	   FAM227A, PDXP, CBY1, TOMM22, Z83844.1, JOSD1, SH3BP1, GTPBP1, AL021707.2, GGA1 
Computing nearest neighbor graph
Computing SNN
Warning: Data is of class matrix. Coercing to dgCMatrix.
Finding variable features for layer counts
Calculating gene variances
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Centering and scaling data matrix

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PC_ 1 
Positive:  TNFSF11, AKAP11, AIMP1, TBCK, GSTCD, INTS12, ARHGEF38, EEF1A1P9, PPA2, TET2 
	   CXXC4, TACR3, CENPE, NOVA1, BDH2, PRKD1, SLC9B2, GNAI3, RTN4IP1, SLC9B1 
	   G2E3, GPR61, QRSL1, VAMP4, CISD2, AMIGO1, SCFD1, PRRC2C, C6orf203, CYB561D1 
Negative:  ODCP, TNPO3, AC025594.1, IRF5, ATP6V1F, KCP, FLNC, CCDC136, CALU, FAM71F2 
	   METTL2B, HILPDA, IMPDH1, RBM28, AC018635.1, LRRC4, SND1, ARF5, GCC1, ZNF800 
	   GRM8, POT1, GPR37, COL6A4P2, PIK3R4, FAM86HP, ATP2C1, ASTE1, ALG1L2, NEK11 
PC_ 2 
Positive:  PSMC4, FCGBP, FBL, ZNF546, DYRK1B, EID2, ZNF780B, EID2B, TDGF1P7, DLL3 
	   TIMM50, ZNF780A, SUPT5H, CNTD2, PLEKHG2, AKT2, ZFP36, C19orf47, MED29, PLD3 
	   PRX, PAF1, SERTAD1, SAMD4B, SERTAD3, BLVRB, GMFG, SPTBN4, SHKBP1, LRFN1 
Negative:  STX7, VNN1, MOXD1, VNN3, CTGF, SLC18B1, ENPP1, TCF21, MED23, TBPL1 
	   ARG1, SLC2A12, EPB41L2, SGK1, SMLR1, HBS1L, TMEM200A, SAMD3, MYB, L3MBTL3 
	   AHI1, ARHGAP18, LINC00271, PTPRK, PDE7B, ECHDC1, RNF146, SOGA3, RSPO3, KIAA0408 
PC_ 3 
Positive:  MAP3K5, MAP7, PEX7, SLC35D3, FEZ2, BCLAF1, MTFR2, VIT, PDE7B, IFNGR1 
	   STRN, LINC00271, HEATR5B, AL357060.1, AHI1, GPATCH11, EIF2AK2, TNFAIP3, MYB, CEBPZ 
	   NDUFAF7, PERP, PRKD3, HBS1L, QPCT, CDC42EP3, RMDN2, CYP1B1, ATL2, RPLP0P6 
Negative:  IARS2, BPNT1, RAB3GAP2, EPRS, MARK1, RIMKLBP2, C1orf115, LYPLAL1, MARC2, TGFB2 
	   MARC1, RRP15, SPATA17, HLX, GPATCH2, DUSP10, ESRRG, KCTD3, HHIPL2, KCNK2 
	   CENPF, TAF1A, PTPN14, SMYD2, PROX1, RPS6KC1, MIA3, ANGEL2, VASH2, FLVCR1 
PC_ 4 
Positive:  AP1B1, RFPL1S, RASL10A, RFPL1, DLG2, TMEM126B, CCDC90B, CREBZF, ANKRD42, EED 
	   PICALM, CCDC81, SYTL2, ME3, PRSS23, GAS2L1, PCF11, OR7E13P, FZD4, TMEM135 
	   RAB30, RAB38, NEFH, PRCP, EWSR1, FAM181B, TENM4, THOC5, RHBDD3, NARS2 
Negative:  FKBP6, BAZ1B, NSUN5, BCL7B, NCF1B, TBL2, STAG3L3, MLXIPL, VPS37D, NSUN5P2 
	   DNAJC30, POM121, STX1A, AC016909.2, NCKAP5, RN7SL625P, MGAT5, ABHD11, LYPD1, TMEM163 
	   ZNF806, CCNT2, SBDSP1, CLDN3, AC097532.2, MAP3K19, ANKRD30BL, CLDN4, RAB3GAP1, RN7SL377P 
PC_ 5 
Positive:  MYB, HBS1L, AHI1, SGK1, LINC00271, SLC2A12, PDE7B, MTFR2, TBPL1, BCLAF1 
	   TCF21, MAP7, MAP3K5, SLC18B1, PEX7, VNN3, TNFAIP3, AL357060.1, PERP, IFNGR1 
	   MARCKSL1P2, SLC35D3, HEBP2, CCDC28A, REPS1, VNN1, ABRACL, HECA, STX7, MOXD1 
Negative:  APOL2, RBFOX2, MYH9, APOL6, TXN2, FOXRED2, EIF3D, CACNG2, IFT27, NCF4 
	   TST, MPST, KCTD17, HORMAD2, IL2RB, AC003681.1, C1QTNF6, MTMR3, ASCC2, RAC2 
	   ZMAT5, FAM92B, KIAA0513, GSE1, ZDHHC7, GINS2, CRISPLD2, C16orf74, USP10, EMC8 
Computing nearest neighbor graph
Computing SNN
Warning: Data is of class matrix. Coercing to dgCMatrix.
Finding variable features for layer counts
Calculating gene variances
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Centering and scaling data matrix

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PC_ 1 
Positive:  ZNF737, ZNF826P, ZNF486, ZNF90, AC006539.1, ZNF682, UNC50, ZNF326, ZNF93, AC011477.1 
	   MGAT4A, ZNF644, ZNF253, ZNF506, YWHAQP5, HFM1, LINC00663, ZNF14, CDC7, KIAA1211L 
	   ZNF101, RTN4IP1, ATP13A1, TSGA10, EPHX4, QRSL1, GMIP, C2orf15, MBLAC1, LAMTOR4 
Negative:  PCOLCE2, TRPC1, PAQR9, PLS1, U2SURP, ATR, CHST2, XRN1, SLC9A9, PRR23C 
	   GK5, COPB2, CLSTN2, MRPS22, C3orf58, ACTG1P1, SLC25A36, TFDP2, PIK3CB, AC107021.1 
	   SPSB4, ATP1B3, RNF7, AC112504.1, RASA2, FAIM, ZBTB38, PLOD2, CEP70, PLSCR1 
PC_ 2 
Positive:  MRGBP, COL9A3, TCFL5, DIDO1, GID8, SLC17A9, YTHDF1, NKAIN4, ARFGAP1, COL20A1 
	   KCNQ2, EEF1A2, PPDPF, PTK6, HELZ2, GMEB2, STMN3, RTEL1, ARFRP1, ZGPAT 
	   INVS, LIME1, ERP44, SLC2A4RG, STX17, ZBTB46, NR4A3, ALG2, TPD52L2, TGFBR1 
Negative:  POLG, TICRR, FES, MAN2A2, BLM, UNC45A, EFCC1, GP9, CRTC3, RAB43 
	   KIF7, KIAA1257, ISY1, ACAD9, HDDC3, CNBP, RAB7A, PEX11A, COPG1, FANCI 
	   HMCES, RCCD1, RPN1, H1FX, RPL32P3, GATA2, PRC1, ABHD2, EFCAB12, EEFSEC 
PC_ 3 
Positive:  TIMP4, PPARG, SYN2, TSEN2, TAMM41, MKRN2, VGLL4, RAF1, TMEM40, ATG7 
	   CAND2, HRH1, IQSEC1, SLC6A11, NUP210, SEC13, HDAC11, GHRL, FBLN2, TATDN2 
	   LINC00620, IRAK2, CHCHD4, VHL, TMEM43, XPC, SLC6A6, GRIP2, CCDC174, FGD5 
Negative:  HEBP2, MARCKSL1P2, CCDC28A, PERP, TNFAIP3, REPS1, AL357060.1, ABRACL, IFNGR1, HECA 
	   SLC35D3, CITED2, PEX7, VTA1, MAP3K5, MVP, PAGR1, CDIPT, MAP7, PRRT2 
	   AIG1, SEZ6L2, MAZ, KIF22, BCLAF1, ASPHD1, ZG16, SLC18B1, TCF21, MTFR2 
PC_ 4 
Positive:  ASCC2, ZMAT5, MTMR3, CABP7, AC003681.1, NF2, HORMAD2, NIPSNAP1, TBC1D10A, THOC5 
	   SF3A1, CCDC157, NEFH, SEC14L2, MTFP1, RFPL1, SEC14L6, RFPL1S, GAL3ST1, AP1B1 
	   PES1, TCN2, SLC35E4, RASL10A, DUSP18, MORC2, TUG1, GAS2L1, SMTN, INPP5J 
Negative:  CGB5, NTF4, CGB2, KCNA7, RUVBL2, SNRNP70, GYS1, LIN7B, BAX, C19orf73 
	   DHDH, PPFIA3, NUCB1, HRC, TULP2, PPP1R15A, TRPM4, PLEKHA4, CD37, HSD17B14 
	   TEAD2, BCAT2, RASIP1, DKKL1, ACAD9, KIAA1257, RAB7A, EFCC1, GP9, RPN1 
PC_ 5 
Positive:  EIF2AK2, CEBPZ, NDUFAF7, GPATCH11, PRKD3, HEATR5B, QPCT, STRN, CDC42EP3, VIT 
	   RMDN2, CYP1B1, FEZ2, ATL2, RPLP0P6, HNRNPLL, CRIM1, GALM, SRSF7, GEMIN6 
	   AC073218.2, SLC3A1, PREPL, CAMKMT, SIX3, SRBD1, PRKCE, EPAS1, ATP6V1E2, RHOQ 
Negative:  RUVBL1, SEC61A1, EEFSEC, MGLL, GATA2, ABTB1, RPN1, RAB7A, ACAD9, KIAA1257 
	   EFCC1, GP9, RAB43, ISY1, CNBP, COPG1, HMCES, H1FX, RPL32P3, EFCAB12 
	   MBD4, IFT122, PLXND1, NF2, NIPSNAP1, TMCC1, THOC5, CABP7, AC083799.1, NEFH 
Computing nearest neighbor graph
Computing SNN
Warning: Data is of class matrix. Coercing to dgCMatrix.
Finding variable features for layer counts
Calculating gene variances
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Centering and scaling data matrix

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PC_ 1 
Positive:  SLC38A9, DNM1L, ARV1, SPRTN, DISC1, CD302, ARL15, KCTD9, FBXO16, KIF13B 
	   AC084262.1, DUSP4, TUBBP1, BRF2, GLB1L3, KDM5A, ERC1, WNT5B, FBXL14, ADIPOR2 
	   DYRK4, AC024940.1, BICD1, SYT10, CNTN1, EXOC8, TSNAX, NTPCR, AC009506.1, LY75 
Negative:  TMEM169, XRCC5, PECR, SMARCAL1, MREG, LINC00607, IGFBP2, FN1, ATIC, IGFBP5 
	   ABCA12, CLPSL1, ARMC12, BARD1, LHFPL5, FKBP5, SRPK1, SPAG16, ARPC2, TULP1 
	   IKZF2, MAPK14, TEAD3, AC079610.1, MAPK13, ERBB4, FANCE, LANCL1, BRPF3, AAMP 
PC_ 2 
Positive:  SLC35G2, STAG1, NCK1, PCNA, PCCB, DZIP1L, TMEM230, MSL2, DBR1, SLC23A2 
	   PPP2R3A, RASSF2, ARMC8, EPHB1, PRNP, SMOX, NME9, CEP63, FTLP3, ANAPC13 
	   MRAS, AMOTL2, ESYT3, RYK, CEP70, CLSTN2, SLCO2A1, SLC25A36, FAIM, RAB6B 
Negative:  PSMC1P1, FAM19A4, EOGT, TMF1, UBA3, ARL6IP5, FRMD4B, MITF, FOXP1, EIF4E3 
	   GPR27, GBE1, CADM2, ROBO1, CHMP2B, AC026877.1, CGGBP1, ROBO2, PROK2, ZNF717 
	   LINC00960, FRG2C, RYBP, ZNF654, RARRES2P1, SHQ1, C3orf38, PPP4R2, AC133041.1, EBLN2 
PC_ 3 
Positive:  PALM3, LDLRAP1, ZNRF2P2, IL27RA, WIPF3, MAN1C1, SCRN1, MTFR1L, RFX1, FKBP14 
	   AL020996.1, PLEKHA8, AUNIP, ZNRF2, DCAF15, PAQR7, NOD1, GGCT, STMN1, GARS 
	   PODNL1, PAFAH2, GHRHR, ADCYAP1R1, SCARNA18, CC2D1A, EXTL1, PPP1R17, AL391650.1, PDIK1L 
Negative:  FAM98A, AC073218.2, RASGRP3, CRIM1, LTBP1, FEZ2, TTC27, VIT, STRN, HEATR5B 
	   MPRIP, RN7SL775P, FLCN, GPATCH11, COPS3, NT5M, MED9, EIF2AK2, RASD1, PEMT 
	   RAI1, CEBPZ, SREBF1, TOM1L2, NDUFAF7, ATPAF2, PRKD3, GID4, DRG2, QPCT 
PC_ 4 
Positive:  TMEM109, PRPF19, CCDC86, MS4A7, MS4A4A, MS4A6A, MS4A3, MRPL16, STX3, AP000640.2 
	   PATL1, OSBP, OR5A2, OR5AN1, CDK12, MPEG1, DTX4, MED1, FAM111A, FAM111B 
	   FBXL20, AP001652.1, CACNB1, GLYATL2, ARL5C, ZFP91, PLXDC1, LINC00672, LPXN, LASP1 
Negative:  CDC37, TYK2, ICAM3, RAVER1, ZGLP1, ICAM4, ICAM1, ELOF1, MRPL4, S1PR2 
	   DNMT1, EIF3G, DOC2B, P2RY11, ZNF846, RPH3AL, C17orf97, FBXL12, VPS53, PPAN 
	   FAM57A, GEMIN4, RPL10P15, DBIL5P, C19orf66, ZNF627, PIN1, GLOD4, C3P1, OLFM2 
PC_ 5 
Positive:  DOC2B, RPH3AL, C17orf97, VPS53, FAM57A, GEMIN4, DBIL5P, GLOD4, NXN, TIMM22 
	   ABR, YWHAE, CRK, MYO1C, INPP5K, PITPNA, SCARF1, RILP, PRPF8, TLCD2 
	   ZNF878, ZNF844, ZNF20, ZNF625, ZNF433, ZNF136, ZNF44, ZNF563, ZNF763, ELOF1 
Negative:  GAL3ST1, SEC14L6, MTFP1, SEC14L2, CCDC157, SF3A1, TBC1D10A, HORMAD2, AC003681.1, MTMR3 
	   TOM1, ASCC2, ZMAT5, HMOX1, CABP7, NF2, NT5M, MED9, RASD1, COPS3 
	   PEMT, RAI1, MYO15A, DRG2, FLCN, GID4, SREBF1, ATPAF2, ALKBH5, FLII 
Computing nearest neighbor graph
Computing SNN
Warning: Data is of class matrix. Coercing to dgCMatrix.
Finding variable features for layer counts
Calculating gene variances
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Centering and scaling data matrix

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PC_ 1 
Positive:  STC1, CENPBD1, SLC25A37, DEF8, ENTPD4, TUBB3, LOXL2, TCF25, DCAF11, PCK2 
	   R3HCC1, SPIRE2, SNX16, NRL, AC009237.14, FANCA, RALYL, CHMP7, FAHD2A, NNAT 
	   DHRS4L2, GPR137, ZNF276, LRRCC1, PROM2, DHRS4, TNFRSF10A, CTNNBL1, VPS9D1, ZNF2 
Negative:  TLK2P1, ASIC2, TMEM98, MYO1D, CCL2, CDK5R1, TMEM132E, PSMD11, ZNF207, C17orf75 
	   RHBDL3, RHOT1, AC090616.2, RALGAPA2, RN7SL690P, CRNKL1, NAA20, RIN2, AL121761.1, AL049647.1 
	   SLC24A3, DTD1, SEC23B, RBBP9, POLR3F, DZANK1, PARD6G, ADNP2, RBFADN, ZNF133 
PC_ 2 
Positive:  AC044860.1, AKAP13, KLHL25, NTRK3, MRPL46, MRPS11, DET1, AC013489.1, AEN, ISG20 
	   ACAN, HAPLN3, CRELD2, MFGE8, ALG12, ABHD2, FANCI, ZBED4, POLG, BRD1 
	   TICRR, KIF7, FAM19A5, PEX11A, TBC1D22A, CERK, GRAMD4, CELSR1, TRMU, CRTC3 
Negative:  AC016722.2, MCFD2, AC016722.1, SOCS5, TTC7A, CRIPT, EPCAM, PIGF, MSH2, RHOQ 
	   KCNK12, ATP6V1E2, EPAS1, AC079250.1, PRKCE, MSH6, SRBD1, FBXO11, SIX3, CAMKMT 
	   AC079807.1, PREPL, SLC3A1, FOXN2, PPP1R21, STON1, LHCGR, FSHR, NRXN1, GEMIN6 
PC_ 3 
Positive:  PEX11A, KIF7, TICRR, ABHD2, FANCI, MFGE8, POLG, HAPLN3, ACAN, ISG20 
	   AEN, AC013489.1, DET1, MRPS11, MRPL46, CRTC3, BLM, NTRK3, FES, MAN2A2 
	   KLHL25, UNC45A, HDDC3, AKAP13, RCCD1, PRC1, AC044860.1, VPS33B, SLCO3A1, ST8SIA2 
Negative:  MS4A4A, MS4A7, MS4A6A, CCDC86, MS4A3, PRPF19, MRPL16, TMEM109, STX3, TMEM132A 
	   AP000640.2, PATL1, SLC15A3, OSBP, CD6, OR5A2, OR5AN1, VPS37C, AP001652.1, FAM111B 
	   GLYATL2, FAM111A, DTX4, ZFP91, MPEG1, LPXN, CTNND1, PGA3, TMX2, MED19 
PC_ 4 
Positive:  MRPL15, SOX17, LYPLA1, XKR4, TMEM68, TCEA1, TGS1, RGS20, LYN, ATP6V1H 
	   RN7SL798P, PLAG1, OPRK1, CHCHD7, RB1CC1, SDR16C5, PCMTD1, IMPAD1, FAM110B, EFCAB1 
	   UBXN2B, SDCBP, NSMAF, TOX, CA8, RAB2A, CHD7, AC022182.2, ASPH, GGH 
Negative:  PTBP1, AZU1, PRSS57, CFD, FSTL3, MED16, RNF126, R3HDM4, POLRMT, KISS1R 
	   BSG, ARID3A, WDR18, CDC34, TMEM259, TPGS1, CNN2, ABCA7, MADCAM1, POLR2E 
	   GPX4, SHC2, SBNO2, STK11, MIER2, MIDN, ABHD17A, SCAMP4, CIRBP, KLF16 
PC_ 5 
Positive:  SEC14L6, GAL3ST1, PES1, INPP5J, SMTN, TUG1, MORC2, DUSP18, SLC35E4, TCN2 
	   PRR14L, DEPDC5, YWHAH, C22orf42, RTCB, FBXO7, SYN3, TIMP3, HMGXB4, TOM1 
	   HMOX1, AC138035.1, TRIM52, ASTE1, NEK11, ATP2C1, MCM5, TRIM41, NUDT16, PIK3R4 
Negative:  CNTN3, PDZRN3, FAM86DP, EBLN2, AC133041.1, PPP4R2, RARRES2P1, SHQ1, FRG2C, LINC00960 
	   ZNF717, RYBP, ROBO2, AC026877.1, PROK2, ROBO1, C3orf38, GBE1, GPR27, CADM2 
	   ZNF654, CHMP2B, CGGBP1, EIF4E3, FOXP1, MITF, FRMD4B, ARL6IP5, UBA3, TMF1 
Computing nearest neighbor graph
Computing SNN
Warning: Data is of class matrix. Coercing to dgCMatrix.
Finding variable features for layer counts
Calculating gene variances
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Centering and scaling data matrix

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PC_ 1 
Positive:  ASH2L, STAR, MYOM1, VWF, ANO2, DPP4, AC013731.1, NTF3, TBR1, CCDC148 
	   KCNA5, ZNF540, PSMD14, VPS26B, GALNT8, AC009299.3, PKP4, NDUFA9, TANC1, AC009299.2 
	   AKAP3, WDSUB1, UNC5D, DCP1B, TMTC1, DDX11, RHOF, EGLN1, SLC35F3, MARCH7 
Negative:  AC025594.1, ATP6V1F, AC018635.1, LRRC4, PODNL1, CC2D1A, SND1, DCAF15, C19orf57, RFX1 
	   RN7SL619P, ARF5, IL27RA, ZSWIM4, PALM3, MRI1, C19orf67, GCC1, SAMD1, CCDC130 
	   PRKACA, ASF1B, ZNF800, KIAA1143, CACNA1A, ZNF502, ZNF501, ZNF35, DDX39A, ZNF197 
PC_ 2 
Positive:  AC009060.1, PDPR, CLEC18A, EXOSC6, WWP2, AARS, NOB1, DDX19B, NQO1, NFAT5 
	   DDX19A, CYB5B, ST3GAL2, TERF2, NIP7, FUK, PDF, COG8, COG4, VPS4A 
	   SF3B3, SNTB2, MTSS1L, VAC14, HYDIN, CMTR2, CHTF8, CALB2, ZNF23, ZNF19 
Negative:  FAM174B, LRRC36, ST8SIA2, PLEKHG4, TPPP3, SLC9A5, FHOD1, SLCO3A1, TMEM208, ZDHHC1 
	   LRRC29, VPS33B, PRC1, HSD11B2, RCCD1, HDDC3, ATP6V0D1, UNC45A, MAN2A2, FES 
	   CTCF, BLM, CRTC3, ACD, PARD6A, PEX11A, ENKD1, CIAPIN1, KIF7, TICRR 
PC_ 3 
Positive:  CARTPT, MAP1B, MCCC2, BDP1, GFOD2, C16orf86, ENKD1, FNBP1P1, MOB1A, TET3 
	   MTHFD2, DGUOK, SLC4A5, STAMBP, DCTN1, DUSP11, C2orf81, ALMS1, AC074008.1, EGR4 
	   PARD6A, FBXO41, CCT7, ACD, PRADC1, SMYD5, CTCF, NOTO, DDX28, RAB11FIP5 
Negative:  STX11, SF3B5, PLAGL1, LTV1, PHACTR2, FUCA2, VDAC1P8, RPTOR, CHMP6, NPTX1 
	   BAIAP2, ENDOV, AATK, RNF213, SLC38A10, SLC26A11, PEX3, SGSH, ACTG1, EIF4A3 
	   NPLOC4, GAA, ADAT2, PDE6G, CCDC40, SMLR1, AIG1, EPB41L2, TBC1D16, VTA1 
PC_ 4 
Positive:  CARTPT, MAP1B, MCCC2, BDP1, PPARD, FANCE, TEAD3, FNBP1P1, TET3, MOB1A 
	   DGUOK, STAMBP, MTHFD2, DUSP11, BRPF3, SLC4A5, PNPLA1, TULP1, DCTN1, MAPK13 
	   C2orf81, ALMS1, MAPK14, AC074008.1, SRPK1, FKBP5, KCTD20, LHFPL5, ARMC12, CLPSL1 
Negative:  TIMP3, HMGXB4, SYN3, TOM1, FBXO7, HMOX1, RTCB, MCM5, C22orf42, RASD2 
	   YWHAH, DEPDC5, MB, PRR14L, APOL6, RBFOX2, APOL2, MYH9, TXN2, FOXRED2 
	   EIF3D, INPP5J, ASCC2, SMTN, ZMAT5, MTMR3, CACNG2, TUG1, CABP7, AC003681.1 
PC_ 5 
Positive:  RN7SL605P, CLUH, PAFAH1B1, RAP1GAP2, METTL16, SGSM2, TSR1, OR3A2, SRR, SMG6 
	   OVCA2, ASPA, DPH1, RPA1, SMYD4, TRPV1, SERPINF1, MIR22HG, TLCD2, SHPK 
	   PRPF8, RILP, CTNS, TAX1BP3, EMC6, P2RX5, ITGAE, CAMKK1, P2RX1, WASH5P 
Negative:  RASL10A, GAS2L1, AP1B1, EWSR1, RFPL1S, RHBDD3, RFPL1, NEFH, EMID1, THOC5 
	   KREMEN1, NIPSNAP1, ZNRF3, NF2, XBP1, CABP7, PCMTD2, CCDC117, MYT1, OPRL1 
	   ZMAT5, HSCB, RGS19, TCEA2, CHEK2, PRPF6, ASCC2, TTC28, SAMD10, ZNF512B 
Computing nearest neighbor graph
Computing SNN
Warning: Data is of class matrix. Coercing to dgCMatrix.
Finding variable features for layer counts
Calculating gene variances
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Centering and scaling data matrix

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PC_ 1 
Positive:  CUL4A, LAMP1, PCID2, RDH10, STAU2, TERF1, F10, UBE2W, GRTP1, TRPA1 
	   TMEM70, EYA1, LY96, F7, ADPRHL1, XKR9, JPH1, LACTB2, GDAP1, MCF2L 
	   CRISPLD1, TRAM1, HNF4G, ATP11A, NCOA2, FAM83F, ZFHX4, GPS1, DUS1L, GRAP2 
Negative:  TG, PHF20L1, TMEM71, LRRC6, HHLA1, EFR3A, B4GALT5, ADCY8, SLC9A8, CCL25 
	   SPATA2, GRINA, ELAVL1, LRRC7, SLC2A10, LRRC40, PTGER3, SRSF11, CTH, HHLA3 
	   TP53RK, ANKRD13C, RNF114, TIMM44, ZRANB2, OPLAH, SLC13A3, WFDC8, NEGR1, WFDC2 
PC_ 2 
Positive:  GPR37, TMEM229A, POT1, WASL, GRM8, IQUB, RPS26P31, ZNF800, CADPS2, GCC1 
	   ARF5, SND1, LRRC4, AC018635.1, RBM28, IMPDH1, HILPDA, METTL2B, FAM71F2, CALU 
	   CCDC136, FLNC, KCP, ATP6V1F, AC025594.1, IRF5, TNPO3, ODCP, TSPAN33, SMO 
Negative:  USP33, TONSL, NEXN, ESCO1, TIE1, C1orf210, POC1B, DUSP6, MPL, GALNT4 
	   KCTD1, KITLG, TP53INP1, ZNF362, ATP2B1, TMTC3, BTG1, CYHR1, TMEM125, CEP290 
	   CDC20, EEA1, DSTYK, C12orf29, NUDT4, GREB1L, AQP4, UBE2N, FUBP1, MGAT4C 
PC_ 3 
Positive:  AEN, PGPEP1L, ISG20, PEX11A, ACAN, CRTC3, HAPLN3, KIF7, BLM, MFGE8 
	   IGF1R, ABHD2, TICRR, FES, FANCI, MAN2A2, POLG, UNC45A, ARRDC4, HDDC3 
	   RCCD1, PRC1, LINC00923, VPS33B, SLCO3A1, ST8SIA2, MCTP2, FAM174B, CHD2, RGMA 
Negative:  RUNDC1, PTGES3L, AARSD1, G6PC, G6PC3, LINC00671, HDAC5, AOC2, C17orf53, PSME3 
	   ASB16, BECN1, LRFN1, GMFG, TMUB2, SAMD4B, PAF1, MED29, ZFP36, ATXN7L3 
	   PLEKHG2, SUPT5H, TIMM50, SERTAD3, SERTAD1, BLVRB, PRX, SPTBN4, PLD3, UBTF 
PC_ 4 
Positive:  TFF2, TMPRSS3, CD14, RSPH1, SLC35A4, SLC37A1, APBB3, PDE9A, WDR4, EIF4EBP3 
	   NDUFV3, PKNOX1, SRA1, CBS, U2AF1, SIK1, EXTL2, ANKHD1, SLC30A7, HSF2BP 
	   DPH5, AC093157.1, H2BFS, HBEGF, S1PR1, RRP1B, RNPC3, AMY2B, PDXK, PRMT6 
Negative:  UBTF, AC003102.1, ATXN7L3, RUNDC3A, TMUB2, SLC25A39, ASB16, GRN, C17orf53, FAM171A2 
	   HDAC5, ITGA2B, G6PC3, PTPRB, LGR5, KCNMB4, ZFC3H1, CNOT2, THAP2, MYRFL 
	   TMEM19, RAB3IP, BEST3, LRRC10, MYF6, LIN7A, PPP1R12A, ACSS3, PAWR, SYT1 
PC_ 5 
Positive:  KCTD3, ESRRG, GPATCH2, SPATA17, RRP15, TGFB2, LYPLAL1, RIMKLBP2, EPRS, BPNT1 
	   IARS2, RAB3GAP2, MARK1, C1orf115, MARC2, MARC1, HLX, DUSP10, EWSR1, RHBDD3 
	   EMID1, EFNA2, MUM1, C19orf24, CIRBP, MIDN, STK11, SBNO2, GPX4, NDUFS7 
Negative:  TFF2, TMPRSS3, RSPH1, CD14, SLC37A1, SLC35A4, PDE9A, APBB3, WDR4, NDUFV3 
	   EIF4EBP3, PKNOX1, CBS, SRA1, U2AF1, SIK1, ANKHD1, HSF2BP, EXTL2, SLC30A7 
	   H2BFS, DPH5, AC093157.1, RRP1B, HBEGF, S1PR1, PDXK, RNPC3, AMY2B, CSTB 
Computing nearest neighbor graph
Computing SNN
mean_delta: 0.0144000477724848, at sigma: 0.008754607423138, and pval: 0.05
KS_delta: 0.00598709803646501, at sigma: 0.008754607423138, and pval: 0.05
In addition: Warning messages:
1: In log2xplus1(infercnv_obj) : NaNs produced
2: package ‘future’ was built under R version 4.4.1 
3: `aes_string()` was deprecated in ggplot2 3.0.0.
ℹ Please use tidy evaluation idioms with `aes()`.
ℹ See also `vignette("ggplot2-in-packages")` for more information.
ℹ The deprecated feature was likely used in the infercnv package.
  Please report the issue at
  <https://github.com/broadinstitute/inferCNV/issues>.
This warning is displayed once every 8 hours.
Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
generated. 
In [82]:
%%R
 plot_cnv(
       infercnv_obj_run,
       out_dir = "output_dir",
       output_filename = "infercnv_plott",
       png_res = 300)
INFO [2025-10-08 15:50:42] ::plot_cnv:Start
INFO [2025-10-08 15:50:42] ::plot_cnv:Current data dimensions (r,c)=14007,575 Total=8060977.94547191 Min=0.827673338207976 Max=1.15777732284435.
INFO [2025-10-08 15:50:43] ::plot_cnv:Depending on the size of the matrix this may take a moment.
INFO [2025-10-08 15:50:43] plot_cnv(): auto thresholding at: (0.968317 , 1.033409)
INFO [2025-10-08 15:50:43] plot_cnv_observation:Start
INFO [2025-10-08 15:50:43] Observation data size: Cells= 498 Genes= 14007
INFO [2025-10-08 15:50:43] plot_cnv_observation:Writing observation groupings/color.
INFO [2025-10-08 15:50:43] plot_cnv_observation:Done writing observation groupings/color.
INFO [2025-10-08 15:50:43] plot_cnv_observation:Writing observation heatmap thresholds.
INFO [2025-10-08 15:50:43] plot_cnv_observation:Done writing observation heatmap thresholds.
INFO [2025-10-08 15:50:44] Colors for breaks:  #00008B,#24249B,#4848AB,#6D6DBC,#9191CC,#B6B6DD,#DADAEE,#FFFFFF,#EEDADA,#DDB6B6,#CC9191,#BC6D6D,#AB4848,#9B2424,#8B0000
INFO [2025-10-08 15:50:44] Quantiles of plotted data range: 0.968317166671738,1.00003843897153,1.00003843897153,1.00003843897153,1.03340940953818
INFO [2025-10-08 15:50:44] plot_cnv_references:Start
INFO [2025-10-08 15:50:44] Reference data size: Cells= 77 Genes= 14007
INFO [2025-10-08 15:50:44] plot_cnv_references:Number reference groups= 1
INFO [2025-10-08 15:50:44] plot_cnv_references:Plotting heatmap.
INFO [2025-10-08 15:50:44] Colors for breaks:  #00008B,#24249B,#4848AB,#6D6DBC,#9191CC,#B6B6DD,#DADAEE,#FFFFFF,#EEDADA,#DDB6B6,#CC9191,#BC6D6D,#AB4848,#9B2424,#8B0000
INFO [2025-10-08 15:50:44] Quantiles of plotted data range: 0.968317166671738,1.00003843897153,1.00003843897153,1.00003843897153,1.03340940953818
$cluster_by_groups
[1] TRUE

$k_obs_groups
[1] 1

$contig_cex
[1] 1

$x.center
[1] 1.000863

$x.range
[1] 0.9683172 1.0334094

$hclust_method
[1] "ward.D"

$color_safe_pal
[1] FALSE

$output_format
[1] "png"

$png_res
[1] 300

$dynamic_resize
[1] 0

Chapter 5: Integration¶

- Since ´infercnv´ was not able to create a full map of CNVs from this data. In this section, I have performed a single cell data integration of Tyser's et al. (2021) data together with Petropolus et. al (2016) data from this study, the data were obtained from this site Petropoulos & Lanner Labs. This site contains data derived from more than 30 papers, refined for integration, including the two studies we're targeting.¶

If you maintained the working directory and downloaded the counts and metadata files for both studies you can run the following lines!¶

In [13]:
#Metadata
metadata_tyser = pd.read_table("Tyser_2021_PMID_34789876.meta.tsv", index_col= "cell")
metadata_petro = pd.read_table("Petropoulos_2016_PMID_27062923.meta.tsv", index_col= "cell")
#counts
counts_tyser = pd.read_csv("Tyser_2021_PMID_34789876.counts.gz", index_col= "Gene")
counts_petro = pd.read_csv("Petropoulos_2016_PMID_27062923.counts.gz", index_col= "Gene")
In [14]:
# Transponse the counts to fit AnnData structure
counts_tyser = counts_tyser.T
counts_petro = counts_petro.T
In [15]:
# Build AnnData objects
adata_petro = ad.AnnData(
    X=counts_petro.values,                 # expression matrix
    obs=metadata_petro,                  # cell metadata
    var=pd.DataFrame(index=counts_petro.columns)  # gene metadata
)

adata_tyser = ad.AnnData(
    X=counts_tyser.values,                 # expression matrix
    obs=metadata_tyser,                  # cell metadata
    var=pd.DataFrame(index=counts_tyser.columns)  # gene metadata
)
In [16]:
adata_petro.var_names_make_unique()
sc.pp.calculate_qc_metrics(adata_petro, inplace=True)
sc.pp.normalize_total(adata_petro)
sc.pp.log1p(adata_petro)
sc.pp.scale(adata_petro, max_value=10)
sc.tl.pca(adata_petro, n_comps=30, random_state=42)
sc.pp.neighbors(adata_petro, n_neighbors=20, n_pcs=30, random_state=42)
sc.tl.umap(adata_petro, random_state=42)
normalizing counts per cell
    finished (0:00:00)
computing PCA
    with n_comps=30
    finished (0:00:01)
computing neighbors
    using 'X_pca' with n_pcs = 30
    finished: added to `.uns['neighbors']`
    `.obsp['distances']`, distances for each pair of neighbors
    `.obsp['connectivities']`, weighted adjacency matrix (0:00:03)
computing UMAP
    finished: added
    'X_umap', UMAP coordinates (adata.obsm)
    'umap', UMAP parameters (adata.uns) (0:00:04)
In [17]:
adata_tyser.var_names_make_unique()
sc.pp.calculate_qc_metrics(adata_tyser, inplace=True)
sc.pp.normalize_total(adata_tyser)
sc.pp.log1p(adata_tyser)
sc.pp.scale(adata_tyser, max_value=10)
sc.tl.pca(adata_tyser, n_comps=30,random_state=42)
sc.pp.neighbors(adata_tyser, n_neighbors=20, n_pcs=30,random_state=42)
sc.tl.umap(adata_tyser,random_state=42)
normalizing counts per cell
    finished (0:00:00)
computing PCA
    with n_comps=30
    finished (0:00:01)
computing neighbors
    using 'X_pca' with n_pcs = 30
    finished: added to `.uns['neighbors']`
    `.obsp['distances']`, distances for each pair of neighbors
    `.obsp['connectivities']`, weighted adjacency matrix (0:00:00)
computing UMAP
    finished: added
    'X_umap', UMAP coordinates (adata.obsm)
    'umap', UMAP parameters (adata.uns) (0:00:04)
In [18]:
sc.pp.highly_variable_genes(adata_petro)
sc.pp.highly_variable_genes(adata_tyser)
extracting highly variable genes
    finished (0:00:00)
--> added
    'highly_variable', boolean vector (adata.var)
    'means', float vector (adata.var)
    'dispersions', float vector (adata.var)
    'dispersions_norm', float vector (adata.var)
extracting highly variable genes
    finished (0:00:00)
--> added
    'highly_variable', boolean vector (adata.var)
    'means', float vector (adata.var)
    'dispersions', float vector (adata.var)
    'dispersions_norm', float vector (adata.var)
In [19]:
sc.tl.leiden(adata_petro, resolution= 0.75, flavor="igraph", n_iterations=2,random_state=42)
sc.tl.leiden(adata_tyser, resolution= 0.75, flavor="igraph", n_iterations=2,random_state=42)
running Leiden clustering
    finished: found 13 clusters and added
    'leiden', the cluster labels (adata.obs, categorical) (0:00:00)
running Leiden clustering
    finished: found 10 clusters and added
    'leiden', the cluster labels (adata.obs, categorical) (0:00:00)
In [20]:
fig, axs = plt.subplots(2, 2, figsize=(10,7), dpi=120)
sc.pl.umap(adata_tyser, color="leiden", ax=axs[0,0], show=False)
sc.pl.umap(adata_tyser, color="raw_annotation", ax=axs[0,1], show=False)
sc.pl.umap(adata_petro, color="leiden", ax=axs[1,0], show=False)
sc.pl.umap(adata_petro, color="raw_annotation", ax=axs[1,1], show=False)
plt.show()
No description has been provided for this image

Maintain shared genes between two objects before integration¶

In [21]:
shared_genes = adata_tyser.var_names.intersection(adata_petro.var_names)
adata_tyser = adata_tyser[:, shared_genes]
adata_petro = adata_petro[:, shared_genes]
In [22]:
print(f"Shared genes: {len(shared_genes)}")
Shared genes: 33501

Merge objects¶

In [23]:
adatas_merged = ad.concat([adata_tyser, adata_petro], merge="same")
#adatas_merged.obs #2363 rows × 17 columns

Apply independent normalization and scaling¶

In [24]:
adatas_merged.var_names_make_unique()
sc.pp.calculate_qc_metrics(adatas_merged, inplace=True)
sc.pp.scale(adatas_merged, max_value=10)
sc.tl.pca(adatas_merged, n_comps=30, random_state=42)
sc.pp.neighbors(adatas_merged, n_neighbors= 50, n_pcs=30, random_state=42)
sc.tl.umap(adatas_merged, random_state=42)

sc.pp.highly_variable_genes(adatas_merged)
sc.tl.leiden(adatas_merged, resolution= 0.75, random_state=42)
/Users/mohammedkhattab/Desktop/venv/lib/python3.12/site-packages/pandas/core/arraylike.py:399: RuntimeWarning: invalid value encountered in log1p
  result = getattr(ufunc, method)(*inputs, **kwargs)
/Users/mohammedkhattab/Desktop/venv/lib/python3.12/site-packages/pandas/core/arraylike.py:399: RuntimeWarning: invalid value encountered in log1p
  result = getattr(ufunc, method)(*inputs, **kwargs)
computing PCA
    with n_comps=30
    finished (0:00:03)
computing neighbors
    using 'X_pca' with n_pcs = 30
    finished: added to `.uns['neighbors']`
    `.obsp['distances']`, distances for each pair of neighbors
    `.obsp['connectivities']`, weighted adjacency matrix (0:00:00)
computing UMAP
    finished: added
    'X_umap', UMAP coordinates (adata.obsm)
    'umap', UMAP parameters (adata.uns) (0:00:22)
extracting highly variable genes
    finished (0:00:01)
--> added
    'highly_variable', boolean vector (adata.var)
    'means', float vector (adata.var)
    'dispersions', float vector (adata.var)
    'dispersions_norm', float vector (adata.var)
running Leiden clustering
/var/folders/sm/v_yn0d7j7xl1n8zc_c47_tgc0000gn/T/ipykernel_8442/573973735.py:9: FutureWarning: In the future, the default backend for leiden will be igraph instead of leidenalg.

 To achieve the future defaults please pass: flavor="igraph" and n_iterations=2.  directed must also be False to work with igraph's implementation.
  sc.tl.leiden(adatas_merged, resolution= 0.75, random_state=42)
    finished: found 11 clusters and added
    'leiden', the cluster labels (adata.obs, categorical) (0:00:00)

Unintegrated¶

In [25]:
fig, axs = plt.subplots(1, 2, figsize=(15, 4), dpi=120)

# Example: one UMAP by dataset, one by raw annotation
sc.pl.umap(adatas_merged, color="leiden", ax=axs[0], show=False)
sc.pl.umap(adatas_merged, color="raw_annotation", ax=axs[1], show=False)

plt.tight_layout()
plt.show()
No description has been provided for this image

Run Scanorama correction¶

In [26]:
adatas = [adata_tyser, adata_petro]

corrected_mats, genes = scanorama.correct(
    [ad.X for ad in adatas],
    [ad.var_names for ad in adatas],
    return_dimred=False
)

# Convert gene list to Index
shared_genes = pd.Index(genes)

# Subset both to the same genes and replace expression
for i in range(len(adatas)):
    adatas[i] = adatas[i][:, shared_genes]
    adatas[i].X = corrected_mats[i]

# Merge integrated AnnData
adata_integrated = adatas[0].concatenate(
    adatas[1],
    batch_key="dataset",
    batch_categories=["Tyser", "Petropoulos"],
    index_unique=None
)

print(adata_integrated)
Found 33501 genes among all datasets
[[0.   0.41]
 [0.   0.  ]]
Processing datasets (0, 1)
/var/folders/sm/v_yn0d7j7xl1n8zc_c47_tgc0000gn/T/ipykernel_8442/1314771505.py:15: UserWarning: Trying to set a dense array with a sparse array on a view.Densifying the sparse array.This may incur excessive memory usage
  adatas[i].X = corrected_mats[i]
/var/folders/sm/v_yn0d7j7xl1n8zc_c47_tgc0000gn/T/ipykernel_8442/1314771505.py:15: ImplicitModificationWarning: Modifying `X` on a view results in data being overridden
  adatas[i].X = corrected_mats[i]
/var/folders/sm/v_yn0d7j7xl1n8zc_c47_tgc0000gn/T/ipykernel_8442/1314771505.py:18: FutureWarning: Use anndata.concat instead of AnnData.concatenate, AnnData.concatenate is deprecated and will be removed in the future. See the tutorial for concat at: https://anndata.readthedocs.io/en/latest/concatenation.html
  adata_integrated = adatas[0].concatenate(
AnnData object with n_obs × n_vars = 2363 × 33501
    obs: 'devTime', 'raw_annotation', 'pred_annotation', 'sub_pred_annotation', 'prediction_score_max', 'pred_psdt', 'proj_UMAP_1', 'proj_UMAP_2', 'n_genes_by_counts', 'log1p_n_genes_by_counts', 'total_counts', 'log1p_total_counts', 'pct_counts_in_top_50_genes', 'pct_counts_in_top_100_genes', 'pct_counts_in_top_200_genes', 'pct_counts_in_top_500_genes', 'leiden', 'dataset'
    var: 'n_cells_by_counts-Petropoulos', 'mean_counts-Petropoulos', 'log1p_mean_counts-Petropoulos', 'pct_dropout_by_counts-Petropoulos', 'total_counts-Petropoulos', 'log1p_total_counts-Petropoulos', 'mean-Petropoulos', 'std-Petropoulos', 'highly_variable-Petropoulos', 'means-Petropoulos', 'dispersions-Petropoulos', 'dispersions_norm-Petropoulos', 'n_cells_by_counts-Tyser', 'mean_counts-Tyser', 'log1p_mean_counts-Tyser', 'pct_dropout_by_counts-Tyser', 'total_counts-Tyser', 'log1p_total_counts-Tyser', 'mean-Tyser', 'std-Tyser', 'highly_variable-Tyser', 'means-Tyser', 'dispersions-Tyser', 'dispersions_norm-Tyser'
    obsm: 'X_pca', 'X_umap'
In [35]:
adata_integrated.shape  # should show (n_cells_total, ~shared_genes)
adata_integrated.obs["dataset"].value_counts()
Out[35]:
dataset
Petropoulos    1193
Tyser          1170
Name: count, dtype: int64
In [36]:
sc.pp.scale(adata_integrated, max_value=10)
sc.tl.pca(adata_integrated, n_comps=30, random_state=42)
sc.pp.neighbors(adata_integrated, n_neighbors= 50, n_pcs=30, random_state=42)
sc.tl.umap(adata_integrated, random_state=42)
computing PCA
    with n_comps=30
    finished (0:00:03)
computing neighbors
    using 'X_pca' with n_pcs = 30
    finished: added to `.uns['neighbors']`
    `.obsp['distances']`, distances for each pair of neighbors
    `.obsp['connectivities']`, weighted adjacency matrix (0:00:00)
computing UMAP
    finished: added
    'X_umap', UMAP coordinates (adata.obsm)
    'umap', UMAP parameters (adata.uns) (0:00:22)

Integrated UMAP¶

In [37]:
fig, axs = plt.subplots(1, 2, figsize=(15, 4), dpi=120)

# Example: one UMAP by dataset, one by raw annotation
sc.pl.umap(adata_integrated, color="dataset", ax=axs[0], show=False)
sc.pl.umap(adata_integrated, color="raw_annotation", ax=axs[1], show=False)

plt.tight_layout()
plt.show()
No description has been provided for this image
In [40]:
sc.pl.umap(adata_integrated, color=["MESP1", "SOX2"], ncols=2)
No description has been provided for this image

Prepare files for inferCNV¶

If wanted save a version of the Integrated AnnData object¶

adata_integrated.write("adata_obj_Integrated.h5ad")

In [42]:
#add metadata
adata_integrated.obs.to_csv("metadata_integrated.csv")
In [104]:
%%R
metadata_integrated <- read_csv("metadata_integrated.csv")
Rows: 2363 Columns: 19
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr  (6): cell, devTime, raw_annotation, pred_annotation, sub_pred_annotatio...
dbl (13): prediction_score_max, pred_psdt, proj_UMAP_1, proj_UMAP_2, n_genes...

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
# A tibble: 2,363 × 19
   cell      devTime raw_annotation pred_annotation sub_pred_annotation
   <chr>     <chr>   <chr>          <chr>           <chr>              
 1 sc7785290 CS7     HEP            HEP             HEP                
 2 sc7786612 CS7     DE             Ambiguous       Ambiguous          
 3 sc7786605 CS7     AdvMes         AdvMes          AdvMes             
 4 sc7785737 CS7     PriS           Ambiguous       Ambiguous          
 5 sc7785398 CS7     ExE_Mes        ExE_Mes         ExE_Mes            
 6 sc7788091 CS7     Axial Mes      Axial Mes       Axial Mes          
 7 sc7785785 CS7     Erythroblasts  Erythroblasts   Erythroblasts      
 8 sc7785959 CS7     ExE_Mes        ExE_Mes         ExE_Mes            
 9 sc7785611 CS7     YSE            YSE             YSE                
10 sc7786585 CS7     Mesoderm       Mesoderm        Mesoderm           
# ℹ 2,353 more rows
# ℹ 14 more variables: prediction_score_max <dbl>, pred_psdt <dbl>,
#   proj_UMAP_1 <dbl>, proj_UMAP_2 <dbl>, n_genes_by_counts <dbl>,
#   log1p_n_genes_by_counts <dbl>, total_counts <dbl>,
#   log1p_total_counts <dbl>, pct_counts_in_top_50_genes <dbl>,
#   pct_counts_in_top_100_genes <dbl>, pct_counts_in_top_200_genes <dbl>,
#   pct_counts_in_top_500_genes <dbl>, leiden <dbl>, dataset <chr>
# ℹ Use `print(n = ...)` to see more rows
In [105]:
annotated_cluster_integrated = adata_integrated.obs[['raw_annotation']].copy()
Out[105]:
raw_annotation
cell
sc7785290 HEP
sc7786612 DE
sc7786605 AdvMes
sc7785737 PriS
sc7785398 ExE_Mes
... ...
E7.9.564 TE
E7.9.567 TE
E7.9.568 TE
E7.9.570 TE
E7.9.573 TE

2363 rows × 1 columns

In [106]:
annotated_cluster_integrated.to_csv("cell_annotations_integrated.tsv", sep="\t", header=False)
In [43]:
merged_counts = pd.concat([counts_petro, counts_tyser], axis=0)
merged_counts #2363 rows × 33501 columns
Out[43]:
Gene MIR1302-2HG FAM138A OR4F5 AL627309.1 AL627309.3 AL627309.2 AL627309.4 AL732372.1 OR4F29 AC114498.1 ... AC007325.2 BX072566.1 AL354822.1 AC023491.2 AC004556.1 AC233755.2 AC233755.1 AC240274.1 AC213203.1 FAM231C
E3.1.443 0 0 0 3 0 0 0 0 0 0 ... 0 0 0 0 0 0 3 9 0 0
E3.1.444 0 0 0 2 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 6 0 0
E3.1.445 0 0 0 18 4 0 0 0 0 0 ... 0 0 0 0 0 0 0 2 0 0
E3.1.447 0 0 0 9 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
E3.1.448 0 0 0 10 0 0 0 0 0 0 ... 0 0 2 0 0 0 0 1 0 0
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
sc7785965 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
sc7788259 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
sc7786123 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
sc7786212 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
sc7785932 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0

2363 rows × 33501 columns

In [109]:
merged_counts.T.to_csv("raw_counts_integrated.tsv", sep="\t")
In [110]:
%%R
raw_counts_int <- read.table("raw_counts_integrated.tsv", sep="\t", header=TRUE, row.names=1, check.names=FALSE)
head(rownames(raw_counts_int))
[1] "MIR1302-2HG" "FAM138A"     "OR4F5"       "AL627309.1"  "AL627309.3" 
[6] "AL627309.2" 
In [111]:
%%R
annotation_file <- read_tsv("cell_annotations_integrated.tsv")
annotation_file
Rows: 2362 Columns: 2
── Column specification ────────────────────────────────────────────────────────
Delimiter: "\t"
chr (2): sc7785290, HEP

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
# A tibble: 2,362 × 2
   sc7785290 HEP          
   <chr>     <chr>        
 1 sc7786612 DE           
 2 sc7786605 AdvMes       
 3 sc7785737 PriS         
 4 sc7785398 ExE_Mes      
 5 sc7788091 Axial Mes    
 6 sc7785785 Erythroblasts
 7 sc7785959 ExE_Mes      
 8 sc7785611 YSE          
 9 sc7786585 Mesoderm     
10 sc7785580 ExE_Mes      
# ℹ 2,352 more rows
# ℹ Use `print(n = ...)` to see more rows
In [ ]:
%%R
#Gene order file found online as mentioned above
gene_order <- read_tsv("gencode_v19_gene_pos.txt")
gene_order
In [113]:
%%R
colnames(gene_order) <- c("gene","chr","start","end")
# Optional: save clean version for inferCNV (no header, 4 cols)
write.table(gene_order, "gene_order_integrated.tsv",
            sep="\t", quote=FALSE, row.names=FALSE, col.names=FALSE)
In [114]:
%%R
#make sure the gene_order have same genes as in raw_counts to prevent errors
gene_order_updated <- subset(gene_order, gene %in% rownames(raw_counts))
gene_order_updated <- gene_order_updated[match(rownames(raw_counts), gene_order_updated$gene), ]
head(gene_order_updated)
# A tibble: 6 × 4
  gene    chr      start      end
  <chr>   <chr>    <dbl>    <dbl>
1 A1BG    chr19 58856544 58864865
2 A1CF    chr10 52559169 52645435
3 A2M     chr12  9220260  9268825
4 A2ML1   chr12  8975068  9039597
5 A2MP1   chr12  9381129  9428413
6 A3GALT2 chr1  33772367 33786699
In [115]:
%%R
# Remove any rows with NA values
gene_order_updated <- na.omit(gene_order_updated)
head(gene_order_updated)
# A tibble: 6 × 4
  gene    chr      start      end
  <chr>   <chr>    <dbl>    <dbl>
1 A1BG    chr19 58856544 58864865
2 A1CF    chr10 52559169 52645435
3 A2M     chr12  9220260  9268825
4 A2ML1   chr12  8975068  9039597
5 A2MP1   chr12  9381129  9428413
6 A3GALT2 chr1  33772367 33786699
In [116]:
%%R
# Keep only genes that exist in gene_order
common_genes <- intersect(rownames(raw_counts), gene_order_updated$gene)
# Subset expression matrix
raw_counts <- raw_counts[common_genes, ]
gene_order_final <- gene_order_updated[match(rownames(raw_counts), gene_order_updated$gene), ]
# Sanity check
all(rownames(raw_counts) == gene_order_final$gene)
[1] TRUE
In [117]:
%%R
# Optional: save clean version for inferCNV (no header, 4 cols)
write.table(gene_order, "gene_order_integrated_final.tsv",
            sep="\t", quote=FALSE, row.names=FALSE, col.names=FALSE)

InferCNV for integrated dataset¶

In [119]:
%%R
infercnv_obj <- CreateInfercnvObject(raw_counts_matrix = as.matrix(raw_counts_int),
                                     annotations_file = "cell_annotations_integrated.tsv",
                                     gene_order_file = "gene_order_integrated_final.tsv",
                                     delim = "\t",
                                     ref_group_names = "Epiblast" )
INFO [2025-10-08 16:10:40] Parsing gene order file: gene_order_integrated_final.tsv
INFO [2025-10-08 16:10:40] Parsing cell annotations file: cell_annotations_integrated.tsv
INFO [2025-10-08 16:10:40] ::order_reduce:Start.
INFO [2025-10-08 16:10:40] .order_reduce(): expr and order match.
INFO [2025-10-08 16:10:40] ::process_data:order_reduce:Reduction from positional data, new dimensions (r,c) = 33501,2363 Total=4698023250 Min=0 Max=507967.
INFO [2025-10-08 16:10:40] num genes removed taking into account provided gene ordering list: 12754 = 38.070505358049% removed.
INFO [2025-10-08 16:10:40] -filtering out cells < 100 or > Inf, removing 0 % of cells
WARN [2025-10-08 16:10:41] Please use "options(scipen = 100)" before running infercnv if you are using the analysis_mode="subclusters" option or you may encounter an error while the hclust is being generated.
INFO [2025-10-08 16:10:42] validating infercnv_obj
In [120]:
%%R
infercnv_obj_run <- infercnv::run(
    infercnv_obj,
    out_dir = "output_dir_integrated",
    cutoff = 1,          # Works well with SmartSeq2
    min_cells_per_gene = 10, # relax cell filter
    HMM = T,
    analysis_mode="subclusters", #inferCNV will attempt to find subpopulations with distinct CNV patterns, rather than assuming each provided group is uniform
    denoise = T)
INFO [2025-10-08 16:10:42] ::process_data:Start
INFO [2025-10-08 16:10:42] Creating output path output_dir_integrated
INFO [2025-10-08 16:10:42] Checking for saved results.
INFO [2025-10-08 16:10:42] 

	STEP 1: incoming data

INFO [2025-10-08 16:11:00] 

	STEP 02: Removing lowly expressed genes

INFO [2025-10-08 16:11:00] ::above_min_mean_expr_cutoff:Start
INFO [2025-10-08 16:11:00] Removing 6993 genes from matrix as below mean expr threshold: 1
INFO [2025-10-08 16:11:00] validating infercnv_obj
INFO [2025-10-08 16:11:00] There are 13754 genes and 2363 cells remaining in the expr matrix.
INFO [2025-10-08 16:11:01] no genes removed due to min cells/gene filter
INFO [2025-10-08 16:11:18] 

	STEP 03: normalization by sequencing depth

INFO [2025-10-08 16:11:18] normalizing counts matrix by depth
INFO [2025-10-08 16:11:19] Computed total sum normalization factor as median libsize: 473728.000000
INFO [2025-10-08 16:11:19] Adding h-spike
INFO [2025-10-08 16:11:19] -hspike modeling of Epiblast
INFO [2025-10-08 16:13:26] validating infercnv_obj
INFO [2025-10-08 16:13:26] normalizing counts matrix by depth
INFO [2025-10-08 16:13:26] Using specified normalization factor: 473728.000000
INFO [2025-10-08 16:13:40] 

	STEP 04: log transformation of data

INFO [2025-10-08 16:13:40] transforming log2xplus1()
INFO [2025-10-08 16:13:40] -mirroring for hspike
INFO [2025-10-08 16:13:40] transforming log2xplus1()
INFO [2025-10-08 16:13:55] 

	STEP 08: removing average of reference data (before smoothing)

INFO [2025-10-08 16:13:55] ::subtract_ref_expr_from_obs:Start inv_log=FALSE, use_bounds=TRUE
INFO [2025-10-08 16:13:55] subtracting mean(normal) per gene per cell across all data
INFO [2025-10-08 16:13:57] -subtracting expr per gene, use_bounds=TRUE
INFO [2025-10-08 16:13:59] -mirroring for hspike
INFO [2025-10-08 16:13:59] ::subtract_ref_expr_from_obs:Start inv_log=FALSE, use_bounds=TRUE
INFO [2025-10-08 16:13:59] subtracting mean(normal) per gene per cell across all data
INFO [2025-10-08 16:14:02] -subtracting expr per gene, use_bounds=TRUE
INFO [2025-10-08 16:14:21] 

	STEP 09: apply max centered expression threshold: 3

INFO [2025-10-08 16:14:21] ::process_data:setting max centered expr, threshold set to: +/-:  3
INFO [2025-10-08 16:14:22] -mirroring for hspike
INFO [2025-10-08 16:14:22] ::process_data:setting max centered expr, threshold set to: +/-:  3
INFO [2025-10-08 16:14:41] 

	STEP 10: Smoothing data per cell by chromosome

INFO [2025-10-08 16:14:41] smooth_by_chromosome: chr: chr1
INFO [2025-10-08 16:14:44] smooth_by_chromosome: chr: chr2
INFO [2025-10-08 16:14:47] smooth_by_chromosome: chr: chr3
INFO [2025-10-08 16:14:49] smooth_by_chromosome: chr: chr4
INFO [2025-10-08 16:14:51] smooth_by_chromosome: chr: chr5
INFO [2025-10-08 16:14:54] smooth_by_chromosome: chr: chr6
INFO [2025-10-08 16:14:55] smooth_by_chromosome: chr: chr7
INFO [2025-10-08 16:14:58] smooth_by_chromosome: chr: chr8
INFO [2025-10-08 16:14:59] smooth_by_chromosome: chr: chr9
INFO [2025-10-08 16:15:01] smooth_by_chromosome: chr: chr10
INFO [2025-10-08 16:15:04] smooth_by_chromosome: chr: chr11
INFO [2025-10-08 16:15:05] smooth_by_chromosome: chr: chr12
INFO [2025-10-08 16:15:07] smooth_by_chromosome: chr: chr13
INFO [2025-10-08 16:15:09] smooth_by_chromosome: chr: chr14
INFO [2025-10-08 16:15:13] smooth_by_chromosome: chr: chr15
INFO [2025-10-08 16:15:14] smooth_by_chromosome: chr: chr16
INFO [2025-10-08 16:15:16] smooth_by_chromosome: chr: chr17
INFO [2025-10-08 16:15:18] smooth_by_chromosome: chr: chr18
INFO [2025-10-08 16:15:20] smooth_by_chromosome: chr: chr19
INFO [2025-10-08 16:15:24] smooth_by_chromosome: chr: chr20
INFO [2025-10-08 16:15:25] smooth_by_chromosome: chr: chr21
INFO [2025-10-08 16:15:27] smooth_by_chromosome: chr: chr22
INFO [2025-10-08 16:15:29] -mirroring for hspike
INFO [2025-10-08 16:15:29] smooth_by_chromosome: chr: chrA
INFO [2025-10-08 16:15:29] smooth_by_chromosome: chr: chr_0
INFO [2025-10-08 16:15:30] smooth_by_chromosome: chr: chr_B
INFO [2025-10-08 16:15:30] smooth_by_chromosome: chr: chr_0pt5
INFO [2025-10-08 16:15:30] smooth_by_chromosome: chr: chr_C
INFO [2025-10-08 16:15:30] smooth_by_chromosome: chr: chr_1pt5
INFO [2025-10-08 16:15:31] smooth_by_chromosome: chr: chr_D
INFO [2025-10-08 16:15:31] smooth_by_chromosome: chr: chr_2pt0
INFO [2025-10-08 16:15:31] smooth_by_chromosome: chr: chr_E
INFO [2025-10-08 16:15:31] smooth_by_chromosome: chr: chr_3pt0
INFO [2025-10-08 16:15:31] smooth_by_chromosome: chr: chr_F
INFO [2025-10-08 16:15:53] 

	STEP 11: re-centering data across chromosome after smoothing

INFO [2025-10-08 16:15:53] ::center_smooth across chromosomes per cell
INFO [2025-10-08 16:15:56] -mirroring for hspike
INFO [2025-10-08 16:15:56] ::center_smooth across chromosomes per cell
INFO [2025-10-08 16:16:17] 

	STEP 12: removing average of reference data (after smoothing)

INFO [2025-10-08 16:16:17] ::subtract_ref_expr_from_obs:Start inv_log=FALSE, use_bounds=TRUE
INFO [2025-10-08 16:16:17] subtracting mean(normal) per gene per cell across all data
INFO [2025-10-08 16:16:19] -subtracting expr per gene, use_bounds=TRUE
INFO [2025-10-08 16:16:21] -mirroring for hspike
INFO [2025-10-08 16:16:21] ::subtract_ref_expr_from_obs:Start inv_log=FALSE, use_bounds=TRUE
INFO [2025-10-08 16:16:21] subtracting mean(normal) per gene per cell across all data
INFO [2025-10-08 16:16:23] -subtracting expr per gene, use_bounds=TRUE
INFO [2025-10-08 16:16:43] 

	STEP 14: invert log2(FC) to FC

INFO [2025-10-08 16:16:43] invert_log2(), computing 2^x
INFO [2025-10-08 16:16:44] -mirroring for hspike
INFO [2025-10-08 16:16:44] invert_log2(), computing 2^x
INFO [2025-10-08 16:17:04] 

	STEP 15: computing tumor subclusters via leiden

INFO [2025-10-08 16:17:04] define_signif_tumor_subclusters(p_val=0.1
INFO [2025-10-08 16:17:05] define_signif_tumor_subclusters(), tumor: 8 cell
INFO [2025-10-08 16:17:05] Setting auto leiden resolution for 8 cell to 0.237326
INFO [2025-10-08 16:17:06] define_signif_tumor_subclusters(), tumor: AdvMes
INFO [2025-10-08 16:17:06] Setting auto leiden resolution for AdvMes to 0.153905
INFO [2025-10-08 16:17:06] define_signif_tumor_subclusters(), tumor: Amnion.Ecto
INFO [2025-10-08 16:17:06] Less cells in group Amnion.Ecto than k_nn setting. Keeping as a single subcluster.
INFO [2025-10-08 16:17:06] define_signif_tumor_subclusters(), tumor: Axial Mes
INFO [2025-10-08 16:17:06] Setting auto leiden resolution for Axial Mes to 0.593499
INFO [2025-10-08 16:17:07] define_signif_tumor_subclusters(), tumor: DE
INFO [2025-10-08 16:17:07] Setting auto leiden resolution for DE to 0.279029
INFO [2025-10-08 16:17:08] define_signif_tumor_subclusters(), tumor: EPI.PrE.INT
INFO [2025-10-08 16:17:08] Less cells in group EPI.PrE.INT than k_nn setting. Keeping as a single subcluster.
INFO [2025-10-08 16:17:08] define_signif_tumor_subclusters(), tumor: Erythroblasts
INFO [2025-10-08 16:17:08] Setting auto leiden resolution for Erythroblasts to 0.430269
INFO [2025-10-08 16:17:09] define_signif_tumor_subclusters(), tumor: ExE_Mes
INFO [2025-10-08 16:17:09] Setting auto leiden resolution for ExE_Mes to 0.125055
INFO [2025-10-08 16:17:10] define_signif_tumor_subclusters(), tumor: HEP
INFO [2025-10-08 16:17:10] Setting auto leiden resolution for HEP to 0.149089
INFO [2025-10-08 16:17:11] define_signif_tumor_subclusters(), tumor: Hypoblast
INFO [2025-10-08 16:17:11] Setting auto leiden resolution for Hypoblast to 0.21198
INFO [2025-10-08 16:17:12] define_signif_tumor_subclusters(), tumor: ICM
INFO [2025-10-08 16:17:12] Setting auto leiden resolution for ICM to 0.468206
INFO [2025-10-08 16:17:13] define_signif_tumor_subclusters(), tumor: Late_Amnion
INFO [2025-10-08 16:17:13] Setting auto leiden resolution for Late_Amnion to 0.482523
INFO [2025-10-08 16:17:13] define_signif_tumor_subclusters(), tumor: Mesoderm
INFO [2025-10-08 16:17:13] Setting auto leiden resolution for Mesoderm to 0.06727
INFO [2025-10-08 16:17:15] define_signif_tumor_subclusters(), tumor: Morula
INFO [2025-10-08 16:17:15] Setting auto leiden resolution for Morula to 0.107509
INFO [2025-10-08 16:17:16] define_signif_tumor_subclusters(), tumor: Prelineage
INFO [2025-10-08 16:17:16] Setting auto leiden resolution for Prelineage to 0.153905
INFO [2025-10-08 16:17:17] define_signif_tumor_subclusters(), tumor: PriS
INFO [2025-10-08 16:17:17] Setting auto leiden resolution for PriS to 0.100576
INFO [2025-10-08 16:17:19] define_signif_tumor_subclusters(), tumor: TE
INFO [2025-10-08 16:17:19] Setting auto leiden resolution for TE to 0.0307126
INFO [2025-10-08 16:17:21] define_signif_tumor_subclusters(), tumor: YSE
INFO [2025-10-08 16:17:21] Setting auto leiden resolution for YSE to 0.283628
INFO [2025-10-08 16:17:22] define_signif_tumor_subclusters(), tumor: Epiblast
INFO [2025-10-08 16:17:22] Setting auto leiden resolution for Epiblast to 0.0852357
INFO [2025-10-08 16:17:23] -mirroring for hspike
INFO [2025-10-08 16:17:23] define_signif_tumor_subclusters(p_val=0.1
INFO [2025-10-08 16:17:23] define_signif_tumor_subclusters(), tumor: spike_tumor_cell_Epiblast
INFO [2025-10-08 16:17:23] cut tree into: 1 groups
INFO [2025-10-08 16:17:23] -processing spike_tumor_cell_Epiblast,spike_tumor_cell_Epiblast_s1
INFO [2025-10-08 16:17:23] define_signif_tumor_subclusters(), tumor: simnorm_cell_Epiblast
INFO [2025-10-08 16:17:23] cut tree into: 1 groups
INFO [2025-10-08 16:17:23] -processing simnorm_cell_Epiblast,simnorm_cell_Epiblast_s1
INFO [2025-10-08 16:17:44] ::plot_cnv:Start
INFO [2025-10-08 16:17:44] ::plot_cnv:Current data dimensions (r,c)=13754,2363 Total=32934377.1150229 Min=0.202126164889467 Max=3.31282454939968.
INFO [2025-10-08 16:17:44] ::plot_cnv:Depending on the size of the matrix this may take a moment.
INFO [2025-10-08 16:17:45] plot_cnv(): auto thresholding at: (0.554558 , 1.472130)
INFO [2025-10-08 16:17:45] plot_cnv_observation:Start
INFO [2025-10-08 16:17:45] Observation data size: Cells= 2152 Genes= 13754
INFO [2025-10-08 16:17:45] clustering observations via method: ward.D
INFO [2025-10-08 16:17:45] Number of cells in group(1) is 41
INFO [2025-10-08 16:17:45] group size being clustered:  41,13754
INFO [2025-10-08 16:17:45] Number of cells in group(2) is 23
INFO [2025-10-08 16:17:45] group size being clustered:  23,13754
INFO [2025-10-08 16:17:46] Number of cells in group(3) is 43
INFO [2025-10-08 16:17:46] group size being clustered:  43,13754
INFO [2025-10-08 16:17:46] Number of cells in group(4) is 31
INFO [2025-10-08 16:17:46] group size being clustered:  31,13754
INFO [2025-10-08 16:17:46] Number of cells in group(5) is 28
INFO [2025-10-08 16:17:46] group size being clustered:  28,13754
INFO [2025-10-08 16:17:46] Number of cells in group(6) is 3
INFO [2025-10-08 16:17:46] group size being clustered:  3,13754
INFO [2025-10-08 16:17:46] Number of cells in group(7) is 1
INFO [2025-10-08 16:17:46] Skipping group: 7, since less than 2 entries
INFO [2025-10-08 16:17:46] Number of cells in group(8) is 20
INFO [2025-10-08 16:17:46] group size being clustered:  20,13754
INFO [2025-10-08 16:17:46] Number of cells in group(9) is 22
INFO [2025-10-08 16:17:46] group size being clustered:  22,13754
INFO [2025-10-08 16:17:46] Number of cells in group(10) is 52
INFO [2025-10-08 16:17:46] group size being clustered:  52,13754
INFO [2025-10-08 16:17:46] Number of cells in group(11) is 1
INFO [2025-10-08 16:17:46] Skipping group: 11, since less than 2 entries
INFO [2025-10-08 16:17:46] Number of cells in group(12) is 16
INFO [2025-10-08 16:17:46] group size being clustered:  16,13754
INFO [2025-10-08 16:17:46] Number of cells in group(13) is 32
INFO [2025-10-08 16:17:46] group size being clustered:  32,13754
INFO [2025-10-08 16:17:46] Number of cells in group(14) is 57
INFO [2025-10-08 16:17:46] group size being clustered:  57,13754
INFO [2025-10-08 16:17:46] Number of cells in group(15) is 38
INFO [2025-10-08 16:17:46] group size being clustered:  38,13754
INFO [2025-10-08 16:17:46] Number of cells in group(16) is 28
INFO [2025-10-08 16:17:46] group size being clustered:  28,13754
INFO [2025-10-08 16:17:46] Number of cells in group(17) is 9
INFO [2025-10-08 16:17:46] group size being clustered:  9,13754
INFO [2025-10-08 16:17:46] Number of cells in group(18) is 2
INFO [2025-10-08 16:17:46] group size being clustered:  2,13754
INFO [2025-10-08 16:17:46] Number of cells in group(19) is 1
INFO [2025-10-08 16:17:46] Skipping group: 19, since less than 2 entries
INFO [2025-10-08 16:17:46] Number of cells in group(20) is 35
INFO [2025-10-08 16:17:46] group size being clustered:  35,13754
INFO [2025-10-08 16:17:46] Number of cells in group(21) is 33
INFO [2025-10-08 16:17:46] group size being clustered:  33,13754
INFO [2025-10-08 16:17:46] Number of cells in group(22) is 23
INFO [2025-10-08 16:17:46] group size being clustered:  23,13754
INFO [2025-10-08 16:17:46] Number of cells in group(23) is 18
INFO [2025-10-08 16:17:46] group size being clustered:  18,13754
INFO [2025-10-08 16:17:46] Number of cells in group(24) is 1
INFO [2025-10-08 16:17:46] Skipping group: 24, since less than 2 entries
INFO [2025-10-08 16:17:46] Number of cells in group(25) is 45
INFO [2025-10-08 16:17:46] group size being clustered:  45,13754
INFO [2025-10-08 16:17:46] Number of cells in group(26) is 28
INFO [2025-10-08 16:17:46] group size being clustered:  28,13754
INFO [2025-10-08 16:17:46] Number of cells in group(27) is 29
INFO [2025-10-08 16:17:46] group size being clustered:  29,13754
INFO [2025-10-08 16:17:46] Number of cells in group(28) is 28
INFO [2025-10-08 16:17:46] group size being clustered:  28,13754
INFO [2025-10-08 16:17:46] Number of cells in group(29) is 95
INFO [2025-10-08 16:17:46] group size being clustered:  95,13754
INFO [2025-10-08 16:17:46] Number of cells in group(30) is 61
INFO [2025-10-08 16:17:46] group size being clustered:  61,13754
INFO [2025-10-08 16:17:46] Number of cells in group(31) is 38
INFO [2025-10-08 16:17:46] group size being clustered:  38,13754
INFO [2025-10-08 16:17:46] Number of cells in group(32) is 33
INFO [2025-10-08 16:17:46] group size being clustered:  33,13754
INFO [2025-10-08 16:17:46] Number of cells in group(33) is 30
INFO [2025-10-08 16:17:46] group size being clustered:  30,13754
INFO [2025-10-08 16:17:46] Number of cells in group(34) is 20
INFO [2025-10-08 16:17:46] group size being clustered:  20,13754
INFO [2025-10-08 16:17:46] Number of cells in group(35) is 1
INFO [2025-10-08 16:17:46] Skipping group: 35, since less than 2 entries
INFO [2025-10-08 16:17:46] Number of cells in group(36) is 61
INFO [2025-10-08 16:17:46] group size being clustered:  61,13754
INFO [2025-10-08 16:17:46] Number of cells in group(37) is 31
INFO [2025-10-08 16:17:46] group size being clustered:  31,13754
INFO [2025-10-08 16:17:46] Number of cells in group(38) is 22
INFO [2025-10-08 16:17:46] group size being clustered:  22,13754
INFO [2025-10-08 16:17:46] Number of cells in group(39) is 19
INFO [2025-10-08 16:17:46] group size being clustered:  19,13754
INFO [2025-10-08 16:17:46] Number of cells in group(40) is 17
INFO [2025-10-08 16:17:46] group size being clustered:  17,13754
INFO [2025-10-08 16:17:46] Number of cells in group(41) is 11
INFO [2025-10-08 16:17:46] group size being clustered:  11,13754
INFO [2025-10-08 16:17:46] Number of cells in group(42) is 46
INFO [2025-10-08 16:17:46] group size being clustered:  46,13754
INFO [2025-10-08 16:17:46] Number of cells in group(43) is 24
INFO [2025-10-08 16:17:46] group size being clustered:  24,13754
INFO [2025-10-08 16:17:46] Number of cells in group(44) is 15
INFO [2025-10-08 16:17:46] group size being clustered:  15,13754
INFO [2025-10-08 16:17:46] Number of cells in group(45) is 11
INFO [2025-10-08 16:17:46] group size being clustered:  11,13754
INFO [2025-10-08 16:17:46] Number of cells in group(46) is 10
INFO [2025-10-08 16:17:46] group size being clustered:  10,13754
INFO [2025-10-08 16:17:46] Number of cells in group(47) is 62
INFO [2025-10-08 16:17:46] group size being clustered:  62,13754
INFO [2025-10-08 16:17:46] Number of cells in group(48) is 60
INFO [2025-10-08 16:17:46] group size being clustered:  60,13754
INFO [2025-10-08 16:17:46] Number of cells in group(49) is 39
INFO [2025-10-08 16:17:47] group size being clustered:  39,13754
INFO [2025-10-08 16:17:47] Number of cells in group(50) is 11
INFO [2025-10-08 16:17:47] group size being clustered:  11,13754
INFO [2025-10-08 16:17:47] Number of cells in group(51) is 1
INFO [2025-10-08 16:17:47] Skipping group: 51, since less than 2 entries
INFO [2025-10-08 16:17:47] Number of cells in group(52) is 1
INFO [2025-10-08 16:17:47] Skipping group: 52, since less than 2 entries
INFO [2025-10-08 16:17:47] Number of cells in group(53) is 90
INFO [2025-10-08 16:17:47] group size being clustered:  90,13754
INFO [2025-10-08 16:17:47] Number of cells in group(54) is 74
INFO [2025-10-08 16:17:47] group size being clustered:  74,13754
INFO [2025-10-08 16:17:47] Number of cells in group(55) is 71
INFO [2025-10-08 16:17:47] group size being clustered:  71,13754
INFO [2025-10-08 16:17:47] Number of cells in group(56) is 70
INFO [2025-10-08 16:17:47] group size being clustered:  70,13754
INFO [2025-10-08 16:17:47] Number of cells in group(57) is 67
INFO [2025-10-08 16:17:47] group size being clustered:  67,13754
INFO [2025-10-08 16:17:47] Number of cells in group(58) is 53
INFO [2025-10-08 16:17:47] group size being clustered:  53,13754
INFO [2025-10-08 16:17:47] Number of cells in group(59) is 49
INFO [2025-10-08 16:17:47] group size being clustered:  49,13754
INFO [2025-10-08 16:17:47] Number of cells in group(60) is 47
INFO [2025-10-08 16:17:47] group size being clustered:  47,13754
INFO [2025-10-08 16:17:47] Number of cells in group(61) is 42
INFO [2025-10-08 16:17:47] group size being clustered:  42,13754
INFO [2025-10-08 16:17:47] Number of cells in group(62) is 39
INFO [2025-10-08 16:17:47] group size being clustered:  39,13754
INFO [2025-10-08 16:17:47] Number of cells in group(63) is 37
INFO [2025-10-08 16:17:47] group size being clustered:  37,13754
INFO [2025-10-08 16:17:47] Number of cells in group(64) is 25
INFO [2025-10-08 16:17:47] group size being clustered:  25,13754
INFO [2025-10-08 16:17:47] Number of cells in group(65) is 21
INFO [2025-10-08 16:17:47] group size being clustered:  21,13754
INFO [2025-10-08 16:17:47] Number of cells in group(66) is 6
INFO [2025-10-08 16:17:47] group size being clustered:  6,13754
INFO [2025-10-08 16:17:47] Number of cells in group(67) is 2
INFO [2025-10-08 16:17:47] group size being clustered:  2,13754
INFO [2025-10-08 16:17:47] Number of cells in group(68) is 35
INFO [2025-10-08 16:17:47] group size being clustered:  35,13754
INFO [2025-10-08 16:17:47] Number of cells in group(69) is 17
INFO [2025-10-08 16:17:47] group size being clustered:  17,13754
INFO [2025-10-08 16:17:47] plot_cnv_observation:Writing observation groupings/color.
INFO [2025-10-08 16:17:47] plot_cnv_observation:Done writing observation groupings/color.
INFO [2025-10-08 16:17:47] plot_cnv_observation:Writing observation heatmap thresholds.
INFO [2025-10-08 16:17:47] plot_cnv_observation:Done writing observation heatmap thresholds.
INFO [2025-10-08 16:17:52] Colors for breaks:  #00008B,#24249B,#4848AB,#6D6DBC,#9191CC,#B6B6DD,#DADAEE,#FFFFFF,#EEDADA,#DDB6B6,#CC9191,#BC6D6D,#AB4848,#9B2424,#8B0000
INFO [2025-10-08 16:17:52] Quantiles of plotted data range: 0.554557588248085,0.904502433693864,1.0000284369913,1.10680317194265,1.47212953469572
INFO [2025-10-08 16:17:55] plot_cnv_references:Start
INFO [2025-10-08 16:17:55] Reference data size: Cells= 211 Genes= 13754
INFO [2025-10-08 16:17:55] plot_cnv_references:Number reference groups= 5
INFO [2025-10-08 16:17:55] plot_cnv_references:Plotting heatmap.
INFO [2025-10-08 16:17:56] Colors for breaks:  #00008B,#24249B,#4848AB,#6D6DBC,#9191CC,#B6B6DD,#DADAEE,#FFFFFF,#EEDADA,#DDB6B6,#CC9191,#BC6D6D,#AB4848,#9B2424,#8B0000
INFO [2025-10-08 16:17:56] Quantiles of plotted data range: 0.554557588248085,0.911539841658008,1.00010701908823,1.09705757707205,1.47212953469572
INFO [2025-10-08 16:18:16] ::plot_cnv:Start
INFO [2025-10-08 16:18:16] ::plot_cnv:Current data dimensions (r,c)=13754,2363 Total=32934377.1150229 Min=0.202126164889467 Max=3.31282454939968.
INFO [2025-10-08 16:18:17] ::plot_cnv:Depending on the size of the matrix this may take a moment.
INFO [2025-10-08 16:18:17] plot_cnv(): auto thresholding at: (0.554558 , 1.472130)
INFO [2025-10-08 16:18:17] plot_cnv_observation:Start
INFO [2025-10-08 16:18:17] Observation data size: Cells= 2152 Genes= 13754
INFO [2025-10-08 16:18:18] plot_cnv_observation:Writing observation groupings/color.
INFO [2025-10-08 16:18:18] plot_cnv_observation:Done writing observation groupings/color.
INFO [2025-10-08 16:18:18] plot_cnv_observation:Writing observation heatmap thresholds.
INFO [2025-10-08 16:18:18] plot_cnv_observation:Done writing observation heatmap thresholds.
INFO [2025-10-08 16:18:22] Colors for breaks:  #00008B,#24249B,#4848AB,#6D6DBC,#9191CC,#B6B6DD,#DADAEE,#FFFFFF,#EEDADA,#DDB6B6,#CC9191,#BC6D6D,#AB4848,#9B2424,#8B0000
INFO [2025-10-08 16:18:22] Quantiles of plotted data range: 0.554557588248085,0.904502433693864,1.0000284369913,1.10680317194265,1.47212953469572
INFO [2025-10-08 16:18:25] plot_cnv_references:Start
INFO [2025-10-08 16:18:25] Reference data size: Cells= 211 Genes= 13754
INFO [2025-10-08 16:18:25] plot_cnv_references:Number reference groups= 1
INFO [2025-10-08 16:18:25] plot_cnv_references:Plotting heatmap.
INFO [2025-10-08 16:18:26] Colors for breaks:  #00008B,#24249B,#4848AB,#6D6DBC,#9191CC,#B6B6DD,#DADAEE,#FFFFFF,#EEDADA,#DDB6B6,#CC9191,#BC6D6D,#AB4848,#9B2424,#8B0000
INFO [2025-10-08 16:18:26] Quantiles of plotted data range: 0.554557588248085,0.911539841658008,1.00010701908823,1.09705757707205,1.47212953469572
INFO [2025-10-08 16:18:26] 

	STEP 17: HMM-based CNV prediction

INFO [2025-10-08 16:18:26] predict_CNV_via_HMM_on_tumor_subclusters
INFO [2025-10-08 16:18:48] -done predicting CNV based on initial tumor subclusters
INFO [2025-10-08 16:18:48] get_predicted_CNV_regions(subcluster)
INFO [2025-10-08 16:18:48] -processing cell_group_name: 8 cell.8 cell_s2, size: 41
INFO [2025-10-08 16:18:53] -processing cell_group_name: 8 cell.8 cell_s1, size: 23
INFO [2025-10-08 16:18:57] -processing cell_group_name: AdvMes.AdvMes_s2, size: 43
INFO [2025-10-08 16:19:02] -processing cell_group_name: AdvMes.AdvMes_s3, size: 31
INFO [2025-10-08 16:19:06] -processing cell_group_name: AdvMes.AdvMes_s1, size: 28
INFO [2025-10-08 16:19:11] -processing cell_group_name: AdvMes.AdvMes_s5, size: 3
INFO [2025-10-08 16:19:15] -processing cell_group_name: AdvMes.AdvMes_s4, size: 1
INFO [2025-10-08 16:19:20] -processing cell_group_name: Amnion.Ecto.Amnion.Ecto, size: 20
INFO [2025-10-08 16:19:24] -processing cell_group_name: Axial Mes.Axial Mes_s1, size: 22
INFO [2025-10-08 16:19:29] -processing cell_group_name: DE.DE_s1, size: 52
INFO [2025-10-08 16:19:34] -processing cell_group_name: DE.DE_s2, size: 1
INFO [2025-10-08 16:19:39] -processing cell_group_name: EPI.PrE.INT.EPI.PrE.INT, size: 16
INFO [2025-10-08 16:19:43] -processing cell_group_name: Erythroblasts.Erythroblasts_s1, size: 32
INFO [2025-10-08 16:19:48] -processing cell_group_name: ExE_Mes.ExE_Mes_s1, size: 57
INFO [2025-10-08 16:19:53] -processing cell_group_name: ExE_Mes.ExE_Mes_s2, size: 38
INFO [2025-10-08 16:19:57] -processing cell_group_name: ExE_Mes.ExE_Mes_s3, size: 28
INFO [2025-10-08 16:20:02] -processing cell_group_name: ExE_Mes.ExE_Mes_s4, size: 9
INFO [2025-10-08 16:20:06] -processing cell_group_name: ExE_Mes.ExE_Mes_s5, size: 2
INFO [2025-10-08 16:20:11] -processing cell_group_name: ExE_Mes.ExE_Mes_s6, size: 1
INFO [2025-10-08 16:20:15] -processing cell_group_name: HEP.HEP_s3, size: 35
INFO [2025-10-08 16:20:20] -processing cell_group_name: HEP.HEP_s2, size: 33
INFO [2025-10-08 16:20:24] -processing cell_group_name: HEP.HEP_s4, size: 23
INFO [2025-10-08 16:20:29] -processing cell_group_name: HEP.HEP_s1, size: 18
INFO [2025-10-08 16:20:34] -processing cell_group_name: HEP.HEP_s5, size: 1
INFO [2025-10-08 16:20:38] -processing cell_group_name: Hypoblast.Hypoblast_s1, size: 45
INFO [2025-10-08 16:20:43] -processing cell_group_name: Hypoblast.Hypoblast_s2, size: 28
INFO [2025-10-08 16:20:47] -processing cell_group_name: ICM.ICM_s1, size: 29
INFO [2025-10-08 16:20:52] -processing cell_group_name: Late_Amnion.Late_Amnion_s1, size: 28
INFO [2025-10-08 16:20:57] -processing cell_group_name: Mesoderm.Mesoderm_s2, size: 95
INFO [2025-10-08 16:21:02] -processing cell_group_name: Mesoderm.Mesoderm_s1, size: 61
INFO [2025-10-08 16:21:07] -processing cell_group_name: Mesoderm.Mesoderm_s4, size: 38
INFO [2025-10-08 16:21:12] -processing cell_group_name: Mesoderm.Mesoderm_s3, size: 33
INFO [2025-10-08 16:21:16] -processing cell_group_name: Mesoderm.Mesoderm_s5, size: 30
INFO [2025-10-08 16:21:21] -processing cell_group_name: Mesoderm.Mesoderm_s7, size: 20
INFO [2025-10-08 16:21:26] -processing cell_group_name: Mesoderm.Mesoderm_s6, size: 1
INFO [2025-10-08 16:21:30] -processing cell_group_name: Morula.Morula_s3, size: 61
INFO [2025-10-08 16:21:35] -processing cell_group_name: Morula.Morula_s2, size: 31
INFO [2025-10-08 16:21:39] -processing cell_group_name: Morula.Morula_s5, size: 22
INFO [2025-10-08 16:21:44] -processing cell_group_name: Morula.Morula_s6, size: 19
INFO [2025-10-08 16:21:49] -processing cell_group_name: Morula.Morula_s4, size: 17
INFO [2025-10-08 16:21:53] -processing cell_group_name: Morula.Morula_s1, size: 11
INFO [2025-10-08 16:21:58] -processing cell_group_name: Prelineage.Prelineage_s2, size: 46
INFO [2025-10-08 16:22:02] -processing cell_group_name: Prelineage.Prelineage_s4, size: 24
INFO [2025-10-08 16:22:07] -processing cell_group_name: Prelineage.Prelineage_s1, size: 15
INFO [2025-10-08 16:22:11] -processing cell_group_name: Prelineage.Prelineage_s3, size: 11
INFO [2025-10-08 16:22:16] -processing cell_group_name: Prelineage.Prelineage_s5, size: 10
INFO [2025-10-08 16:22:20] -processing cell_group_name: PriS.PriS_s1, size: 62
INFO [2025-10-08 16:22:25] -processing cell_group_name: PriS.PriS_s3, size: 60
INFO [2025-10-08 16:22:30] -processing cell_group_name: PriS.PriS_s2, size: 39
INFO [2025-10-08 16:22:35] -processing cell_group_name: PriS.PriS_s4, size: 11
INFO [2025-10-08 16:22:40] -processing cell_group_name: PriS.PriS_s5, size: 1
INFO [2025-10-08 16:22:44] -processing cell_group_name: PriS.PriS_s6, size: 1
INFO [2025-10-08 16:22:48] -processing cell_group_name: TE.TE_s11, size: 90
INFO [2025-10-08 16:22:53] -processing cell_group_name: TE.TE_s4, size: 74
INFO [2025-10-08 16:22:58] -processing cell_group_name: TE.TE_s3, size: 71
INFO [2025-10-08 16:23:04] -processing cell_group_name: TE.TE_s6, size: 70
INFO [2025-10-08 16:23:09] -processing cell_group_name: TE.TE_s13, size: 67
INFO [2025-10-08 16:23:14] -processing cell_group_name: TE.TE_s10, size: 53
INFO [2025-10-08 16:23:18] -processing cell_group_name: TE.TE_s1, size: 49
INFO [2025-10-08 16:23:23] -processing cell_group_name: TE.TE_s2, size: 47
INFO [2025-10-08 16:23:28] -processing cell_group_name: TE.TE_s15, size: 42
INFO [2025-10-08 16:23:32] -processing cell_group_name: TE.TE_s14, size: 39
INFO [2025-10-08 16:23:37] -processing cell_group_name: TE.TE_s12, size: 37
INFO [2025-10-08 16:23:42] -processing cell_group_name: TE.TE_s7, size: 25
INFO [2025-10-08 16:23:47] -processing cell_group_name: TE.TE_s8, size: 21
INFO [2025-10-08 16:23:51] -processing cell_group_name: TE.TE_s5, size: 6
INFO [2025-10-08 16:23:57] -processing cell_group_name: TE.TE_s9, size: 2
INFO [2025-10-08 16:24:03] -processing cell_group_name: YSE.YSE_s1, size: 35
INFO [2025-10-08 16:24:09] -processing cell_group_name: YSE.YSE_s2, size: 17
INFO [2025-10-08 16:24:14] -processing cell_group_name: Epiblast.Epiblast_s4, size: 74
INFO [2025-10-08 16:24:22] -processing cell_group_name: Epiblast.Epiblast_s1, size: 71
INFO [2025-10-08 16:24:27] -processing cell_group_name: Epiblast.Epiblast_s2, size: 58
INFO [2025-10-08 16:24:33] -processing cell_group_name: Epiblast.Epiblast_s3, size: 7
INFO [2025-10-08 16:24:37] -processing cell_group_name: Epiblast.Epiblast_s5, size: 1
INFO [2025-10-08 16:24:42] -writing cell clusters file: output_dir_integrated/17_HMM_predHMMi6.leiden.hmm_mode-subclusters.cell_groupings
INFO [2025-10-08 16:24:42] -writing cnv regions file: output_dir_integrated/17_HMM_predHMMi6.leiden.hmm_mode-subclusters.pred_cnv_regions.dat
INFO [2025-10-08 16:24:43] -writing per-gene cnv report: output_dir_integrated/17_HMM_predHMMi6.leiden.hmm_mode-subclusters.pred_cnv_genes.dat
INFO [2025-10-08 16:24:44] -writing gene ordering info: output_dir_integrated/17_HMM_predHMMi6.leiden.hmm_mode-subclusters.genes_used.dat
INFO [2025-10-08 16:24:56] ::plot_cnv:Start
INFO [2025-10-08 16:24:56] ::plot_cnv:Current data dimensions (r,c)=13754,2363 Total=98919851 Min=2 Max=6.
INFO [2025-10-08 16:24:56] ::plot_cnv:Depending on the size of the matrix this may take a moment.
INFO [2025-10-08 16:24:57] plot_cnv_observation:Start
INFO [2025-10-08 16:24:57] Observation data size: Cells= 2152 Genes= 13754
INFO [2025-10-08 16:24:57] plot_cnv_observation:Writing observation groupings/color.
INFO [2025-10-08 16:24:57] plot_cnv_observation:Done writing observation groupings/color.
INFO [2025-10-08 16:24:57] plot_cnv_observation:Writing observation heatmap thresholds.
INFO [2025-10-08 16:24:57] plot_cnv_observation:Done writing observation heatmap thresholds.
INFO [2025-10-08 16:25:01] Colors for breaks:  #00008B,#24249B,#4848AB,#6D6DBC,#9191CC,#B6B6DD,#DADAEE,#FFFFFF,#EEDADA,#DDB6B6,#CC9191,#BC6D6D,#AB4848,#9B2424,#8B0000
INFO [2025-10-08 16:25:01] Quantiles of plotted data range: 2,3,3,3,6
INFO [2025-10-08 16:25:03] plot_cnv_references:Start
INFO [2025-10-08 16:25:03] Reference data size: Cells= 211 Genes= 13754
INFO [2025-10-08 16:25:04] plot_cnv_references:Number reference groups= 1
INFO [2025-10-08 16:25:04] plot_cnv_references:Plotting heatmap.
INFO [2025-10-08 16:25:04] Colors for breaks:  #00008B,#24249B,#4848AB,#6D6DBC,#9191CC,#B6B6DD,#DADAEE,#FFFFFF,#EEDADA,#DDB6B6,#CC9191,#BC6D6D,#AB4848,#9B2424,#8B0000
INFO [2025-10-08 16:25:04] Quantiles of plotted data range: 2,3,3,3,6
INFO [2025-10-08 16:25:05] 

	STEP 18: Run Bayesian Network Model on HMM predicted CNVs

INFO [2025-10-08 16:25:05] Creating the following Directory:  output_dir_integrated/BayesNetOutput.HMMi6.leiden.hmm_mode-subclusters
INFO [2025-10-08 16:25:05] Initializing new MCM InferCNV Object.
INFO [2025-10-08 16:25:05] validating infercnv_obj
INFO [2025-10-08 16:25:06] Total CNV's:  1970
INFO [2025-10-08 16:25:06] Loading BUGS Model.
INFO [2025-10-08 16:25:07] Running Sampling Using Parallel with  4 Cores
INFO [2025-10-08 16:52:09] Obtaining probabilities post-sampling
INFO [2025-10-08 16:53:50] Gibbs sampling time:  28.7153206825256  Minutes
INFO [2025-10-08 16:56:52] ::plot_cnv:Start
INFO [2025-10-08 16:56:52] ::plot_cnv:Current data dimensions (r,c)=13754,2363 Total=2423104.67580038 Min=0 Max=0.98513614063991.
INFO [2025-10-08 16:56:53] ::plot_cnv:Depending on the size of the matrix this may take a moment.
INFO [2025-10-08 16:56:54] plot_cnv_observation:Start
INFO [2025-10-08 16:56:54] Observation data size: Cells= 2152 Genes= 13754
INFO [2025-10-08 16:56:54] plot_cnv_observation:Writing observation groupings/color.
INFO [2025-10-08 16:56:54] plot_cnv_observation:Done writing observation groupings/color.
INFO [2025-10-08 16:56:54] plot_cnv_observation:Writing observation heatmap thresholds.
INFO [2025-10-08 16:56:54] plot_cnv_observation:Done writing observation heatmap thresholds.
INFO [2025-10-08 16:56:58] Colors for breaks:  #00008B,#24249B,#4848AB,#6D6DBC,#9191CC,#B6B6DD,#DADAEE,#FFFFFF,#EEDADA,#DDB6B6,#CC9191,#BC6D6D,#AB4848,#9B2424,#8B0000
INFO [2025-10-08 16:56:58] Quantiles of plotted data range: 0,0,0,0,0.98513614063991
INFO [2025-10-08 16:57:00] plot_cnv_references:Start
INFO [2025-10-08 16:57:00] Reference data size: Cells= 211 Genes= 13754
INFO [2025-10-08 16:57:00] plot_cnv_references:Number reference groups= 1
INFO [2025-10-08 16:57:00] plot_cnv_references:Plotting heatmap.
INFO [2025-10-08 16:57:01] Colors for breaks:  #00008B,#24249B,#4848AB,#6D6DBC,#9191CC,#B6B6DD,#DADAEE,#FFFFFF,#EEDADA,#DDB6B6,#CC9191,#BC6D6D,#AB4848,#9B2424,#8B0000
INFO [2025-10-08 16:57:01] Quantiles of plotted data range: 0,0,0,0,0.924577526141671
INFO [2025-10-08 16:57:42] 

	STEP 19: Filter HMM predicted CNVs based on the Bayesian Network Model results and BayesMaxPNormal

INFO [2025-10-08 16:57:43] Attempting to removing CNV(s) with a probability of being normal above  0.5
INFO [2025-10-08 16:57:43] Removing  13  CNV(s) identified by the HMM.
INFO [2025-10-08 16:57:43] Total CNV's after removing:  1957
INFO [2025-10-08 16:57:43] Reassigning CNVs based on state probabilities.
INFO [2025-10-08 16:57:43] Changing the following CNV's states assigned by the HMM to the following based on the CNV's state probabilities.
 chr3-region_119 : 4  (P= 0.405983230281579 ) ->  3 (P= 0.42121403711082 )
chr3-region_124 : 5  (P= 0.283935789976073 ) ->  4 (P= 0.295544990319134 )
chr6-region_137 : 2  (P= 0.379528294059109 ) ->  3 (P= 0.481661644994921 )
chr9-region_157 : 4  (P= 0.369931945292196 ) ->  3 (P= 0.424836685215562 )
chr11-region_169 : 2  (P= 0.419406917772341 ) ->  3 (P= 0.443199452965677 )
chr12-region_180 : 5  (P= 0.276399181672451 ) ->  4 (P= 0.378758505125002 )
chr19-region_205 : 4  (P= 0.406106160305132 ) ->  3 (P= 0.449925530366546 )
chr19-region_207 : 4  (P= 0.242153484347523 ) ->  5 (P= 0.343821157537603 )
chr19-region_210 : 5  (P= 0.275678135147369 ) ->  4 (P= 0.276167504097574 )
chr19-region_211 : 4  (P= 0.252005298913088 ) ->  3 (P= 0.317173008991551 )
chr22-region_218 : 4  (P= 0.343504175108546 ) ->  3 (P= 0.448340193611606 )
chr6-region_230 : 4  (P= 0.351277129123129 ) ->  3 (P= 0.440018195789041 )
chr17-region_306 : 4  (P= 0.387268534302063 ) ->  3 (P= 0.478069281981049 )
chr3-region_326 : 4  (P= 0.413932664934364 ) ->  3 (P= 0.439601663314522 )
chr1-region_368 : 4  (P= 0.224223823387291 ) ->  3 (P= 0.338620855406641 )
chr3-region_375 : 4  (P= 0.277429685770763 ) ->  3 (P= 0.278709598875707 )
chr5-region_388 : 4  (P= 0.222472727906337 ) ->  3 (P= 0.331973157085 )
chr6-region_391 : 4  (P= 0.111074188636388 ) ->  3 (P= 0.333447313790936 )
chr7-region_398 : 4  (P= 0.110843549508546 ) ->  3 (P= 0.332679540457241 )
chr8-region_576 : 2  (P= 0.421086864596863 ) ->  3 (P= 0.424400795667482 )
chr9-region_578 : 2  (P= 0.416667734895305 ) ->  3 (P= 0.428803806317556 )
chr8-region_809 : 4  (P= 0.276314031937032 ) ->  3 (P= 0.363258251706968 )
chr15-region_865 : 4  (P= 0.423610543337225 ) ->  3 (P= 0.456322693934349 )
chr11-region_968 : 4  (P= 0.35361003417681 ) ->  3 (P= 0.432153957469345 )
chr18-region_981 : 4  (P= 0.372226939692477 ) ->  3 (P= 0.434921903878718 )
chr2-region_1046 : 4  (P= 0.274171708063671 ) ->  3 (P= 0.390686532909412 )
chr9-region_1081 : 4  (P= 0.328354445816868 ) ->  3 (P= 0.333676323763557 )
chr13-region_1093 : 4  (P= 0.332328179875219 ) ->  3 (P= 0.399724030008997 )
chr1-region_1125 : 5  (P= 0.24940539810775 ) ->  4 (P= 0.249496776373159 )
chr2-region_1132 : 5  (P= 0.239607763703251 ) ->  4 (P= 0.249324326736286 )
chr4-region_1146 : 4  (P= 0.250270238933685 ) ->  3 (P= 0.251700596709065 )
chr7-region_1164 : 4  (P= 0.2025035448253 ) ->  3 (P= 0.250834326898898 )
chr7-region_1165 : 5  (P= 0.249540216093397 ) ->  4 (P= 0.249759801782465 )
chr12-region_1187 : 4  (P= 0.127371674427561 ) ->  5 (P= 0.251262172932175 )
chr15-region_1194 : 4  (P= 0.249432694249778 ) ->  3 (P= 0.252498431405592 )
chr15-region_1196 : 4  (P= 0.248571271884588 ) ->  3 (P= 0.252238846732552 )
chr15-region_1198 : 4  (P= 0.243420412387099 ) ->  3 (P= 0.251848967904412 )
chr17-region_1208 : 4  (P= 0.247888717372483 ) ->  3 (P= 0.25420922170597 )
chr18-region_1212 : 5  (P= 0.125962796488537 ) ->  6 (P= 0.250948891243788 )
chr19-region_1216 : 2  (P= 0.247975228200793 ) ->  3 (P= 0.247994048233104 )
chr19-region_1220 : 2  (P= 0.247682021332085 ) ->  3 (P= 0.255913413320256 )
chr3-region_1245 : 2  (P= 0.147275623479186 ) ->  3 (P= 0.285495730095783 )
chr6-region_1452 : 4  (P= 0.344446706098626 ) ->  3 (P= 0.379109091877934 )
chr16-region_1473 : 4  (P= 0.413614424073976 ) ->  3 (P= 0.413849320671028 )
chr22-region_1702 : 4  (P= 0.444384538981841 ) ->  3 (P= 0.477511052213738 )
chr11-region_1726 : 4  (P= 0.440579231742526 ) ->  3 (P= 0.44182930919806 )
chr12-region_1729 : 4  (P= 0.395569632654088 ) ->  3 (P= 0.469767295036065 )
chr15-region_1777 : 2  (P= 0.400225289142585 ) ->  3 (P= 0.486251774353427 )
chr15-region_1779 : 2  (P= 0.420412401132769 ) ->  3 (P= 0.46587110090427 )
chr22-region_1803 : 4  (P= 0.400707911721438 ) ->  3 (P= 0.485746431078324 )
chr6-region_1856 : 4  (P= 0.365590485568002 ) ->  3 (P= 0.434168748129988 )
chr15-region_1928 : 4  (P= 0.429315480088322 ) ->  3 (P= 0.447228832093577 )
chr6-region_2052 : 4  (P= 0.307710168865647 ) ->  3 (P= 0.333554227854005 )
chr9-region_2108 : 2  (P= 0.346703286697403 ) ->  3 (P= 0.499045432973857 )
chr5-region_2271 : 2  (P= 0.446096635536903 ) ->  3 (P= 0.494316456670122 )
chr17-region_2311 : 5  (P= 0.353560326843123 ) ->  4 (P= 0.450810609658249 )
chr8-region_2442 : 4  (P= 0.322203153435541 ) ->  3 (P= 0.356794644860704 )
chr1-region_2577 : 2  (P= 0.391966121037366 ) ->  3 (P= 0.433625630811318 )
chr6-region_2592 : 2  (P= 0.390218033481026 ) ->  3 (P= 0.436245948275901 )
chr6-region_2596 : 2  (P= 0.389876275410445 ) ->  3 (P= 0.434527010714195 )
chr14-region_2620 : 4  (P= 0.304857035928724 ) ->  3 (P= 0.392620208854509 )
chr17-region_2633 : 4  (P= 0.348825307751822 ) ->  3 (P= 0.39068449126752 )
chr21-region_2647 : 4  (P= 0.351476189948465 ) ->  3 (P= 0.382573894188479 )
chr1-region_2653 : 5  (P= 0.232820586530421 ) ->  4 (P= 0.356116249241823 )
chr2-region_2658 : 2  (P= 0.178158856972292 ) ->  3 (P= 0.466600015776938 )
chr4-region_2665 : 4  (P= 0.292934721155979 ) ->  5 (P= 0.352484642956504 )
chr5-region_2668 : 2  (P= 0.35311736537782 ) ->  3 (P= 0.410398240882965 )
chr6-region_2672 : 4  (P= 0.29163176389675 ) ->  3 (P= 0.356466029645783 )
chr7-region_2675 : 4  (P= 0.233823672310192 ) ->  3 (P= 0.41269987834234 )
chr8-region_2678 : 4  (P= 0.294410547989494 ) ->  3 (P= 0.352666363895105 )
chr9-region_2682 : 4  (P= 0.2934583982015 ) ->  3 (P= 0.413701911328675 )
chr10-region_2685 : 4  (P= 0.188338486535807 ) ->  3 (P= 0.411602446400542 )
chr22-region_2731 : 2  (P= 0.294634384555265 ) ->  3 (P= 0.471280020694834 )
chr4-region_2744 : 5  (P= 0.335636794378098 ) ->  4 (P= 0.4892367455599 )
chr17-region_2863 : 5  (P= 0.334197416693962 ) ->  4 (P= 0.507073593138038 )
chr4-region_3016 : 2  (P= 0.374776618039341 ) ->  3 (P= 0.37498282733221 )
chr2-region_3093 : 4  (P= 0.426925967816796 ) ->  3 (P= 0.441288598087111 )
chr15-region_3138 : 4  (P= 0.441872037774394 ) ->  3 (P= 0.455724121085901 )
chr19-region_3147 : 2  (P= 0.450124896752441 ) ->  3 (P= 0.491504602634677 )
chr21-region_3155 : 4  (P= 0.425434256658998 ) ->  3 (P= 0.455582712404893 )
chr2-region_3189 : 4  (P= 0.406301636515673 ) ->  3 (P= 0.460086919245965 )
chr5-region_3200 : 4  (P= 0.419906564216863 ) ->  3 (P= 0.445966238590655 )
chr6-region_3206 : 5  (P= 0.177967386753564 ) ->  4 (P= 0.410756179784966 )
chr20-region_3235 : 2  (P= 0.421702503362424 ) ->  3 (P= 0.489959346424674 )
chr6-region_3255 : 5  (P= 0.293172778413102 ) ->  4 (P= 0.293781746807126 )
chr19-region_3399 : 2  (P= 0.146620252241022 ) ->  3 (P= 0.280165620376585 )
chr19-region_3666 : 2  (P= 0.453392828046475 ) ->  3 (P= 0.494330050845097 )
chr7-region_3824 : 2  (P= 0.433438196188996 ) ->  3 (P= 0.499011256793833 )
chr1-region_3919 : 5  (P= 0.306325511814273 ) ->  4 (P= 0.461718050549069 )
chr1-region_3922 : 4  (P= 0.395439472930336 ) ->  3 (P= 0.481017054204587 )
chr11-region_3975 : 5  (P= 0.359987228457572 ) ->  4 (P= 0.382803011826221 )
chr12-region_3979 : 2  (P= 0.455424781096251 ) ->  3 (P= 0.46869079275771 )
chr2-region_4113 : 4  (P= 0.40191261649827 ) ->  3 (P= 0.404565641338224 )
chr11-region_4294 : 2  (P= 0.370046421085695 ) ->  3 (P= 0.481497647165132 )
chr12-region_4299 : 4  (P= 0.373124908150481 ) ->  3 (P= 0.442375965034522 )
chr17-region_4308 : 4  (P= 0.333729124107909 ) ->  3 (P= 0.443888215439595 )
chr1-region_4329 : 4  (P= 0.251893205832908 ) ->  3 (P= 0.332833242411248 )
chr6-region_4341 : 4  (P= 0.254012103495276 ) ->  3 (P= 0.332562310875634 )
chr11-region_4354 : 4  (P= 0.251279012098997 ) ->  3 (P= 0.333486637881748 )
chr16-region_4369 : 4  (P= 0.332834245755072 ) ->  3 (P= 0.336156107990993 )
chr17-region_4376 : 4  (P= 0.0855598390483393 ) ->  3 (P= 0.499288246636372 )
chr12-region_4422 : 2  (P= 0.250679809506509 ) ->  3 (P= 0.252034265793059 )
chr12-region_4424 : 4  (P= 0.249852285420866 ) ->  3 (P= 0.249946742002308 )
chr21-region_4450 : 4  (P= 0.248308698260772 ) ->  3 (P= 0.253537263610524 )
chr6-region_4559 : 4  (P= 0.320469286885272 ) ->  3 (P= 0.412950398430061 )
chr2-region_4579 : 2  (P= 0.467516444291257 ) ->  3 (P= 0.480577044422975 )
chr1-region_4650 : 5  (P= 0.155501110233243 ) ->  6 (P= 0.304532414739023 )
chr1-region_4653 : 4  (P= 0.229566101255422 ) ->  3 (P= 0.460907610578825 )
chr1-region_4663 : 2  (P= 0.229938742886836 ) ->  3 (P= 0.460979894441005 )
chr2-region_4665 : 2  (P= 0.30698493367828 ) ->  3 (P= 0.38241929200582 )
chr4-region_4678 : 2  (P= 0.30571078612881 ) ->  3 (P= 0.385698317092313 )
chr8-region_4708 : 5  (P= 0.231602767793764 ) ->  6 (P= 0.306938703874863 )
chr9-region_4712 : 5  (P= 0.229262838971002 ) ->  4 (P= 0.387833854031756 )
chr16-region_4742 : 4  (P= 0.306770320841458 ) ->  5 (P= 0.307558375102484 )
chr16-region_4752 : 4  (P= 0.23426953827699 ) ->  5 (P= 0.306133744120125 )
chr17-region_4758 : 5  (P= 0.321238195979 ) ->  4 (P= 0.370316511023943 )
chr17-region_4759 : 4  (P= 0.320127532141369 ) ->  5 (P= 0.37089216983982 )
chr18-region_4761 : 2  (P= 0.308340788486555 ) ->  3 (P= 0.385002439964717 )
chr19-region_4764 : 4  (P= 0.152229888392134 ) ->  5 (P= 0.463040905048986 )
chr21-region_4777 : 4  (P= 0.232451461281039 ) ->  5 (P= 0.308402958076632 )
INFO [2025-10-08 16:57:43] Creating Plots for CNV and cell Probabilities.
INFO [2025-10-08 17:09:13] ::plot_cnv:Start
INFO [2025-10-08 17:09:13] ::plot_cnv:Current data dimensions (r,c)=13754,2363 Total=2405115.59252957 Min=0 Max=0.98513614063991.
INFO [2025-10-08 17:09:13] ::plot_cnv:Depending on the size of the matrix this may take a moment.
INFO [2025-10-08 17:09:13] plot_cnv_observation:Start
INFO [2025-10-08 17:09:13] Observation data size: Cells= 2152 Genes= 13754
INFO [2025-10-08 17:09:14] plot_cnv_observation:Writing observation groupings/color.
INFO [2025-10-08 17:09:14] plot_cnv_observation:Done writing observation groupings/color.
INFO [2025-10-08 17:09:14] plot_cnv_observation:Writing observation heatmap thresholds.
INFO [2025-10-08 17:09:14] plot_cnv_observation:Done writing observation heatmap thresholds.
INFO [2025-10-08 17:09:18] Colors for breaks:  #00008B,#24249B,#4848AB,#6D6DBC,#9191CC,#B6B6DD,#DADAEE,#FFFFFF,#EEDADA,#DDB6B6,#CC9191,#BC6D6D,#AB4848,#9B2424,#8B0000
INFO [2025-10-08 17:09:18] Quantiles of plotted data range: 0,0,0,0,0.98513614063991
INFO [2025-10-08 17:09:20] plot_cnv_references:Start
INFO [2025-10-08 17:09:20] Reference data size: Cells= 211 Genes= 13754
INFO [2025-10-08 17:09:20] plot_cnv_references:Number reference groups= 1
INFO [2025-10-08 17:09:20] plot_cnv_references:Plotting heatmap.
INFO [2025-10-08 17:09:20] Colors for breaks:  #00008B,#24249B,#4848AB,#6D6DBC,#9191CC,#B6B6DD,#DADAEE,#FFFFFF,#EEDADA,#DDB6B6,#CC9191,#BC6D6D,#AB4848,#9B2424,#8B0000
INFO [2025-10-08 17:09:20] Quantiles of plotted data range: 0,0,0,0,0.924577526141671
INFO [2025-10-08 17:09:33] ::plot_cnv:Start
INFO [2025-10-08 17:09:33] ::plot_cnv:Current data dimensions (r,c)=13754,2363 Total=98928502 Min=2 Max=6.
INFO [2025-10-08 17:09:33] ::plot_cnv:Depending on the size of the matrix this may take a moment.
INFO [2025-10-08 17:09:33] plot_cnv_observation:Start
INFO [2025-10-08 17:09:33] Observation data size: Cells= 2152 Genes= 13754
INFO [2025-10-08 17:09:33] plot_cnv_observation:Writing observation groupings/color.
INFO [2025-10-08 17:09:33] plot_cnv_observation:Done writing observation groupings/color.
INFO [2025-10-08 17:09:34] plot_cnv_observation:Writing observation heatmap thresholds.
INFO [2025-10-08 17:09:34] plot_cnv_observation:Done writing observation heatmap thresholds.
INFO [2025-10-08 17:09:38] Colors for breaks:  #00008B,#24249B,#4848AB,#6D6DBC,#9191CC,#B6B6DD,#DADAEE,#FFFFFF,#EEDADA,#DDB6B6,#CC9191,#BC6D6D,#AB4848,#9B2424,#8B0000
INFO [2025-10-08 17:09:38] Quantiles of plotted data range: 2,3,3,3,6
INFO [2025-10-08 17:09:40] plot_cnv_references:Start
INFO [2025-10-08 17:09:40] Reference data size: Cells= 211 Genes= 13754
INFO [2025-10-08 17:09:40] plot_cnv_references:Number reference groups= 1
INFO [2025-10-08 17:09:40] plot_cnv_references:Plotting heatmap.
INFO [2025-10-08 17:09:41] Colors for breaks:  #00008B,#24249B,#4848AB,#6D6DBC,#9191CC,#B6B6DD,#DADAEE,#FFFFFF,#EEDADA,#DDB6B6,#CC9191,#BC6D6D,#AB4848,#9B2424,#8B0000
INFO [2025-10-08 17:09:41] Quantiles of plotted data range: 2,3,3,3,6
INFO [2025-10-08 17:09:47] 

	STEP 20: Converting HMM-based CNV states to repr expr vals

INFO [2025-10-08 17:10:00] ::plot_cnv:Start
INFO [2025-10-08 17:10:00] ::plot_cnv:Current data dimensions (r,c)=13754,2363 Total=33218051 Min=0.5 Max=3.
INFO [2025-10-08 17:10:00] ::plot_cnv:Depending on the size of the matrix this may take a moment.
INFO [2025-10-08 17:10:00] plot_cnv_observation:Start
INFO [2025-10-08 17:10:00] Observation data size: Cells= 2152 Genes= 13754
INFO [2025-10-08 17:10:01] plot_cnv_observation:Writing observation groupings/color.
INFO [2025-10-08 17:10:01] plot_cnv_observation:Done writing observation groupings/color.
INFO [2025-10-08 17:10:01] plot_cnv_observation:Writing observation heatmap thresholds.
INFO [2025-10-08 17:10:01] plot_cnv_observation:Done writing observation heatmap thresholds.
INFO [2025-10-08 17:10:05] Colors for breaks:  #00008B,#24249B,#4848AB,#6D6DBC,#9191CC,#B6B6DD,#DADAEE,#FFFFFF,#EEDADA,#DDB6B6,#CC9191,#BC6D6D,#AB4848,#9B2424,#8B0000
INFO [2025-10-08 17:10:05] Quantiles of plotted data range: 0.5,1,1,1,3
INFO [2025-10-08 17:10:07] plot_cnv_references:Start
INFO [2025-10-08 17:10:07] Reference data size: Cells= 211 Genes= 13754
INFO [2025-10-08 17:10:08] plot_cnv_references:Number reference groups= 1
INFO [2025-10-08 17:10:08] plot_cnv_references:Plotting heatmap.
INFO [2025-10-08 17:10:09] Colors for breaks:  #00008B,#24249B,#4848AB,#6D6DBC,#9191CC,#B6B6DD,#DADAEE,#FFFFFF,#EEDADA,#DDB6B6,#CC9191,#BC6D6D,#AB4848,#9B2424,#8B0000
INFO [2025-10-08 17:10:09] Quantiles of plotted data range: 0.5,1,1,1,3
INFO [2025-10-08 17:10:09] 

	STEP 22: Denoising

INFO [2025-10-08 17:10:09] ::process_data:Remove noise, noise threshold defined via ref mean sd_amplifier:  1.5
INFO [2025-10-08 17:10:09] denoising using mean(normal) +- sd_amplifier * sd(normal) per gene per cell across all data
INFO [2025-10-08 17:10:09] :: **** clear_noise_via_ref_quantiles **** : removing noise between bounds:  0.791835523440265 - 1.22959436441825
INFO [2025-10-08 17:10:33] 

## Making the final infercnv heatmap ##
INFO [2025-10-08 17:10:34] ::plot_cnv:Start
INFO [2025-10-08 17:10:34] ::plot_cnv:Current data dimensions (r,c)=13754,2363 Total=33244923.5683827 Min=0.202126164889467 Max=3.31282454939968.
INFO [2025-10-08 17:10:34] ::plot_cnv:Depending on the size of the matrix this may take a moment.
INFO [2025-10-08 17:10:35] plot_cnv(): auto thresholding at: (0.527870 , 1.472130)
INFO [2025-10-08 17:10:36] plot_cnv_observation:Start
INFO [2025-10-08 17:10:36] Observation data size: Cells= 2152 Genes= 13754
INFO [2025-10-08 17:10:36] plot_cnv_observation:Writing observation groupings/color.
INFO [2025-10-08 17:10:36] plot_cnv_observation:Done writing observation groupings/color.
INFO [2025-10-08 17:10:36] plot_cnv_observation:Writing observation heatmap thresholds.
INFO [2025-10-08 17:10:36] plot_cnv_observation:Done writing observation heatmap thresholds.
INFO [2025-10-08 17:10:41] Colors for breaks:  #00008B,#24249B,#4848AB,#6D6DBC,#9191CC,#B6B6DD,#DADAEE,#FFFFFF,#EEDADA,#DDB6B6,#CC9191,#BC6D6D,#AB4848,#9B2424,#8B0000
INFO [2025-10-08 17:10:41] Quantiles of plotted data range: 0.527870465304278,1.01071494392926,1.01071494392926,1.01071494392926,1.47212953469572
INFO [2025-10-08 17:10:43] plot_cnv_references:Start
INFO [2025-10-08 17:10:43] Reference data size: Cells= 211 Genes= 13754
INFO [2025-10-08 17:10:44] plot_cnv_references:Number reference groups= 1
INFO [2025-10-08 17:10:44] plot_cnv_references:Plotting heatmap.
INFO [2025-10-08 17:10:44] Colors for breaks:  #00008B,#24249B,#4848AB,#6D6DBC,#9191CC,#B6B6DD,#DADAEE,#FFFFFF,#EEDADA,#DDB6B6,#CC9191,#BC6D6D,#AB4848,#9B2424,#8B0000
INFO [2025-10-08 17:10:44] Quantiles of plotted data range: 0.527870465304278,1.01071494392926,1.01071494392926,1.01071494392926,1.47212953469572
Warning: Data is of class matrix. Coercing to dgCMatrix.
Finding variable features for layer counts
Calculating gene variances
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Centering and scaling data matrix

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PC_ 1 
Positive:  CAPN10, RNPEPL1, KIF1A, DUSP28, ANKMY1, PASK, GPC1, NDUFA10, PPP1R7, HDAC4 
	   ANO7, ASB1, TRAF3IP1, KIAA0930, FAM118A, SMC1B, NUP50, RIBC2, PER2, FBLN1 
	   HDLBP, ATXN10, PHF21B, WNT7B, PPARA, ARHGAP8, CDPF1, TTC38, HES6, GTSE1 
Negative:  ZNF610, ZNF480, ZNF766, PPP2R1A, ZNF836, GABBR1, ZNF616, MOG, ZNF841, ZNF432 
	   ZFP57, ZNF614, HLA-F, ZNF615, ZSCAN4, ZNF551, ZNF154, ZNF211, HLA-G, ZNF134 
	   ZNF350, ZNF256, ZNF613, HLA-A, C19orf18, ZNF44, ZNF563, ZNF606, ZNF136, ZNF442 
PC_ 2 
Positive:  DTL, INTS7, PPP2R5A, TMEM206, LPGAT1, NENF, NEK2, SLC30A1, ATF3, LINC00467 
	   BATF3, TRAF5, RCOR3, KCNH1, NSL1, HHAT, SERTAD4, TATDN3, SYT14, FLVCR1 
	   IRF6, C1orf74, LAMB3, TRAF3IP3, G0S2, PLXNA2, CD34, VASH2, CD46, CR1L 
Negative:  KLK7, KLK6, KLK8, KLK2, C19orf48, KLK10, GPR32, CLEC11A, KLK11, C19orf81 
	   JOSD2, KLK13, EMC10, CTU1, FAM71E1, VSIG10L, SPIB, ETFB, LIM2, POLD1 
	   SIGLEC10, NAPSA, ZNF175, NR1H2, ZNF577, KCNC3, ZNF649, ZNF613, ZNF473, ZNF350 
PC_ 3 
Positive:  EGFLAM, LIFR, LIFR-AS1, OSMR, RICTOR, DAB2, PTGER4, TTC33, PRKAA1, RPL37 
	   OXCT1, C5orf51, FBXO4, GHR, CCDC152, ANXA2R, ZNF131, HMGCS1, CCL28, C5orf34 
	   PAIP1, NNT-AS1, NNT, MRPS30, HCN1, EMB, TIGIT, ZDHHC23, GRAMD1C, ATP6V1A 
Negative:  EML2, GPR4, GIPR, OPA3, SNRPD2, VASP, PPM1N, QPCTL, FOSB, RTN2 
	   FBXO46, SIX5, DMPK, ERCC1, DMWD, SYMPK, FOXA3, CD3EAP, IRF2BP1, MYPOP 
	   GDF15, PGPEP1, SSBP4, PPP1R13L, LSM4, CCDC61, ISYNA1, PPP5C, CCDC8, HIF3A 
PC_ 4 
Positive:  IFI6, FAM76A, STX12, PPP1R8, THEMIS2, RPA2, SMPDL3B, SFPQ, ZMYM4, KIAA0319L 
	   NCDN, ZMYM1, XKR8, PSMB2, C1orf216, ZMYM6, CLSPN, DLGAP3, AGO4, AGO1 
	   GJA4, EYA3, SH3D21, AGO3, EVA1B, GJB3, THRAP3, TEKT2, STK40, ADPRHL2 
Negative:  BMPR2, FAM117B, ICA1L, NOP58, SUMO1, AC079354.3, WDR12, FZD7, CARF, ALS2 
	   NBEAL1, TMEM237, STRADB, CYP20A1, TRAK2, CASP8, ABI2, CASP10, RAPH1, PARD3B 
	   CFLAR-AS1, NRP2, CFLAR, INO80D, NDUFB3, AC007383.3, FAM126B, NDUFS1, ORC2, NIF3L1 
PC_ 5 
Positive:  HIGD2A, CLTB, FAF2, RNF44, GPRIN1, SNCB, EIF4E1B, TSPAN17, UIMC1, ZNF346 
	   FGFR4, NSD1, RAB24, MXD3, PRELID1, LMAN2, RGS14, F12, GRK6, PCDHGA12 
	   PRR7, PCDHGA10, DBN1, PCDHGA2, PDLIM7, TAF7, DOK3, PCDHB5, PCDHB3, DDX41 
Negative:  LRFN5, C14orf28, FBXO33, MIA2, KLHL28, PNN, PRPF39, TRAPPC6B, FKBP3, FANCM 
	   MIS18BP1, RPL10L, GEMIN2, MDGA2, KIAA0319L, ZMYM4, LINC00648, RPS29, CLSPN, NCDN 
	   AGO4, C1orf216, AL139099.1, PSMB2, SEC23A, SFPQ, AGO1, ZMYM1, MIPOL1, ZMYM6 
Computing nearest neighbor graph
Computing SNN
Warning: Data is of class matrix. Coercing to dgCMatrix.
Finding variable features for layer counts
Calculating gene variances
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Centering and scaling data matrix

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PC_ 1 
Positive:  IL11RA, DNAJB5, GALT, VCP, SIGMAR1, FANCG, DCTN3, PIGO, RPP25L, CNTFR 
	   STOML2, ENHO, FAM214B, DNAI1, UNC13B, FAM219A, RUSC2, C9orf24, NUDT2, TESK1 
	   KIF24, CD72, UBAP1, RMRP, DCAF12, UBAP2, CCDC107, UBE2R2, ARHGEF39, DLGAP4 
Negative:  ENY2, NUDCD1, PKHD1L1, TMEM74, EBAG9, SYBU, EMC2, KCNV1, TRPS1, EIF3E 
	   EIF3H, UTP23, RSPO2, RAD21, SLC30A8, ANGPT1, MED30, EXT1, SAMD12, OXR1 
	   TNFRSF11B, ZFPM2, COLEC10, MAL2, LRP12, ENPP2, DPYS, TAF2, RIMS2, DSCC1 
PC_ 2 
Positive:  CTU1, VSIG10L, ETFB, LIM2, SIGLEC10, ZNF175, ZNF577, ZNF649, ZNF613, ZNF350 
	   ZNF615, ZNF614, ZNF432, ZNF841, ZNF616, ZNF836, ZNF324, ZNF446, PPP2R1A, ZNF766 
	   ZNF480, ZNF610, SLC25A1, DGCR2, USP18, TUBA8, PEX26, MICAL3, CLCF1, RPS6KB2 
Negative:  ZNF644, ZNF326, CDC7, TGFBR3, LRRC8D, BRDT, LRRC8C, EPHX4, LRRC8B, BTBD8 
	   C1orf146, GBP4, GLMN, GBP2, RPAP2, GBP1, GFI1, RBMXL1, EVI5, RPL5 
	   GTF2B, FAM69A, MTF2, CCDC18, TMED5, PKN2, DR1, FNBP1L, LMO4, BCAR3 
PC_ 3 
Positive:  AZI2, CMC1, RBMS3, EOMES, TGFBR2, SLC4A7, STT3B, LRRC3B, OSBPL10, OXSM 
	   ZNF860, NGLY1, GPD1L, TOP2B, RARB, THRB, CMTM8, NR1D2, RPL15, CMTM7 
	   CMTM6, NKIRAS1, DYNC1LI1, UBE2E1, CNOT10, UBE2E2, TRIM71, ZNF385D-AS1, GLB1, KAT2B 
Negative:  RNLS, PTEN, STAMBPL1, KLLN, ATAD1, PAPSS2, ACTA2, MINPP1, NUTM2A-AS1, LIPA 
	   GLUD1, IFIT3, IFIT1, SNCG, IFIT5, MMRN2, PANK1, BMPR1A, KIF20B, RPP30 
	   CCSER2, PCGF5, ANKRD1, HECTD2, PPP1R3C, LINC00858, TNKS2, CDHR1, BTAF1, CPEB3 
PC_ 4 
Positive:  STXBP6, NOVA1, SDR39U1, FOXG1, PRKD1, KHNYN, NYNRIN, G2E3, LTB4R, SCFD1 
	   LTB4R2, COCH, CIDEB, NOP9, DHRS1, RABGGTA, TGM1, TINF2, GMPR2, NEDD8 
	   NEDD8-MDP1, MDP1, CHMP4A, NUP43, LATS1, PCMT1, LRP11, GINM1, KATNA1, PPP1R14C 
Negative:  NDUFB5, USP13, MRPL47, ACTL6A, TTC14, STRA6, ISLR, CCDC33, CCDC39, CYP11A1 
	   PML, SEMA7A, FXR1, UBL7, STOML1, UBL7-AS1, ARID3B, DNAJC19, LOXL1, CLK3 
	   EDC3, SOX2, LOXL1-AS1, ATP8B1, NARS, CYP1A1, NEDD4L, FECH, LINC-ROR, CSK 
PC_ 5 
Positive:  ARHGAP19-SLIT1, LCOR, PIK3AP1, TM9SF3, ARHGAP19, FRAT1, TLL2, FRAT2, ZNF518A, CCNJ 
	   RRP12, CC2D2B, PGAM1, ENTPD1-AS1, EXOSC1, ENTPD1, ZDHHC16, TCTN3, MMS19, ALDH18A1 
	   UBTD1, SORBS1, ANKRD2, PDLIM1, PI4K2A, CYP2C18, MORN4, EFNA4, EFNA3, ADAM15 
Negative:  BRF2, RAB11FIP1, EIF4EBP1, ASH2L, STAR, ERLIN2, LETM2, FGFR1, DDHD2, ZNF703 
	   LSM1, BAG4, TACC1, UNC5D, PLEKHA2, RNF122, MAK16, HTRA4, TTI2, FUT10 
	   NRG1, WRN, TM2D2, TEX15, PPP2CB, ADAM9, UBXN8, ADAM32, GSR, ADAM18 
Computing nearest neighbor graph
Computing SNN
Warning: Data is of class matrix. Coercing to dgCMatrix.
Finding variable features for layer counts
Calculating gene variances
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Centering and scaling data matrix

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PC_ 1 
Positive:  VCAN, XRCC4, TMEM167A, FBXO4, C5orf51, GHR, CCL28, CCDC152, ANXA2R, ZNF131 
	   HMGCS1, C5orf34, ATP6AP1L, OXCT1, PAIP1, RPL37, NNT-AS1, OSMR, LIFR-AS1, RPS23 
	   LIFR, PRKAA1, RICTOR, DAB2, PTGER4, TTC33, NNT, EGFLAM, WDR70, MRPS30 
Negative:  HDAC5, G6PC3, C17orf53, LSM12, SSBP4, GDF15, ISYNA1, TMEM101, PGPEP1, LSM4 
	   JUND, PDE4C, RAB3A, MPV17L2, NAGS, IFI30, PYY, PIK3R2, MAST3, PPY 
	   MPP2, ARRDC2, MPP3, KCNN1, DUSP3, CCDC124, ETV4, SLC5A5, DHX8, RPL18A 
PC_ 2 
Positive:  INPP5A, NKX6-2, UTF1, VENTX, ADAM8, PWWP2B, TUBGCP2, ZNF511, LRRC27, PRAP1 
	   STK32C, DPYSL4, FUOM, BNIP3, ECHS1, PPP2R2D, PAOX, TCERG1L, GLRX3, MTG1 
	   MGMT, DCP1A, CACNA1D, MKI67, ERC2, CHDH, IL17RB, CCDC66, TKT, PTPRE 
Negative:  HMGA1, NUDT3, GRM4, RPS10-NUDT3, LEMD2, RPS10, PACSIN1, UQCC2, C6orf106, SNRPC 
	   ITPR3, UHRF1BP1, BAK1, TAF11, ANKS1A, TCP11, ZBTB9, SCUBE3, CUTA, PHF1 
	   KIFC1, DAXX, ZBTB22, TAPBP, RGL2, PFDN6, WDR46, MICAL3, PEX26, TUBA8 
PC_ 3 
Positive:  ACOX2, KCTD6, PDHB, C3orf67, FHIT, PXK, PTPRG, RPP14, ABHD6, DNASE1L3 
	   FLNB-AS1, FLNB, SLMAP, DENND6A, AC022400.2, ARF4, AC022400.1, SEC24C, USP54, MYOZ1 
	   PPP3CB, MSS51, ANXA7, PDE12, DNAJC9-AS1, NEU1, MRPS16, SLC44A4, C6orf48, EHMT2 
Negative:  ENTPD1-AS1, CC2D2B, ALDH18A1, CCNJ, SORBS1, ZNF518A, PDLIM1, CYP2C18, HELLS, TLL2 
	   TBC1D12, NOC3L, TM9SF3, PLCE1, SLC35G1, PIK3AP1, LGI1, FRA10AC1, LCOR, PDE6C 
	   RBP4, CEP55, MYOF, ARHGAP19-SLIT1, CYP26A1, EXOC6, ARHGAP19, HHEX, FRAT1, KIF11 
PC_ 4 
Positive:  AQR, ACTC1, GOLGA8B, ZNF770, GOLGA8A, NANOGP8, DPH6, C15orf41, LPCAT4, MEIS2 
	   SPRED1, NUTM1, FAM98B, NOP10, RASGRP1, SLC12A6, THBS1, EMC4, FSIP1, KATNBL1 
	   GPR176, PGBD4, EIF2AK4, SRP14, EMC7, SRP14-AS1, BMF, FRMD6, TMX1, GNG2 
Negative:  IPO7, ZNF143, TMEM41B, SWAP70, WEE1, SBF2-AS1, DENND5A, SCUBE2, SBF2, NRIP3 
	   TMEM9B-AS1, ADM, AMPD3, TMEM9B, C11orf16, MTRNR2L8, RPL27A, AKIP1, ST5, TRIM66 
	   STK33, RNF141, LMO1, RIC3, TUB, MRVI1-AS1, LYVE1, MRVI1, CTR9, EIF4G2 
PC_ 5 
Positive:  PITRM1, PFKP, PITRM1-AS1, ZMYND11, KLF6, AKR1E2, LINC00200, DIP2C, IDI1, IDI2-AS1 
	   WDR37, CDK4, PRR26, GTPBP4, MARCH9, AKR1C3, TSPAN31, AGAP2-AS1, PIP4K2C, B4GALNT1 
	   LARP4B, OS9, CYP27B1, KIF5A, DCTN2, MBD6, TUBAL3, METTL1, DDIT3, NET1 
Negative:  TMEM189-UBE2V1, TMEM189, SNAI1, UBE2V1, RNF114, SPATA2, SLC9A8, CEBPB, B4GALT5, PTGIS 
	   PTPN1, KCNB1, ZFAS1, PARD6B, BCAS4, ADNP, DPM1, MOCS3, ZNFX1, KCNG1 
	   NFATC2, DDX27, ATP9A, STAU1, SALL4, ZFP64, ZNF217, BCAS1, CSE1L, PFDN4 
Computing nearest neighbor graph
Computing SNN
Warning: Data is of class matrix. Coercing to dgCMatrix.
Finding variable features for layer counts
Calculating gene variances
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Centering and scaling data matrix

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PC_ 1 
Positive:  PSD4, IL1RN, IL36RN, PAX8, IL1A, CKAP2L, CBWD2, RABL2A, SLC20A1, CHCHD5 
	   POLR1B, SLC35F5, TTL, RGPD8, ACTR3, ZC3H6, ZC3H8, DPP10, FBLN7, TMEM87B 
	   DDX18, MERTK, ANAPC1, CCDC93, BCL2L11, ACOXL, INSIG2, BUB1, NPHP1, EN1 
Negative:  ARFGAP1, KCNQ2, NKAIN4, EEF1A2, YTHDF1, TCFL5, GID8, COL9A3, DIDO1, PPDPF 
	   GMEB2, STMN3, RTEL1, HDGF, RTEL1-TNFRSF6B, PRCC, ARHGEF11, ETV3, CD1D, IFI16 
	   CRP, DUSP23, NDUFS8, TCIRG1, UNC93B1, ALDH3B2, CHKA, LRP5, C11orf24, ACY3 
PC_ 2 
Positive:  HAUS8, MYO9B, CPAMD8, USE1, SIN3B, TMEM38A, SMIM7, OCEL1, NR2F6, MED26 
	   USHBP1, BABAM1, SLC35E1, ANKLE1, CHERP, ABHD8, C19orf44, MRPL34, EPS15L1, DDA1 
	   KLF2, GTPBP3, AP1M1, PLVAP, FAM32A, BST2, RAB8A, MVB12A, TPM4, SLC27A1 
Negative:  HAX1, UBAP2L, ATP8B2, C1orf43, TPM3, IL6R, RPS27, RAB13, SHE, JTB 
	   UBE2Q1, CREB3L4, SLC39A1, ADAR, CRTC2, DENND4B, PMVK, GATAD2B, PBXIP1, SLC27A3 
	   PYGO2, INTS3, SERPINH1, MAP6, DGAT2, GDPD5, UVRAG, KLHL35, WNT11, RPS3 
PC_ 3 
Positive:  PCDH18, PCDH10, C4orf33, TRA2A, CCDC126, IGF2BP3, FAM221A, SCLT1, STK31, PGRMC2 
	   NPY, MALSU1, LARP1B, GPNMB, MPP6, MFSD8, NUPL2, PLK4, HSPA4L, OSBPL3 
	   SLC25A31, KLHL7, INTU, ANKRD50, SPRY1, SPATA5, NUDT6, ADAD1, BBS12, FGF2 
Negative:  LHPP, FAM53B, ZRANB1, CTBP2, NKX1-2, OAT, UROS, CHST15, BCCIP, CPXM2 
	   DHX32, FANK1, ADAM12, BUB3, C10orf90, ACADSB, IKZF5, DOCK1, FAM196A, PTPRE 
	   PSTK, MKI67, MGMT, GLRX3, C10orf88, TCERG1L, FAM24B, PPP2R2D, CUZD1, BNIP3 
PC_ 4 
Positive:  C16orf72, GRIN2A, ATF7IP2, EMP2, TEKT5, NUBP1, TVP23A, CIITA, DEXI, IPO7 
	   TMEM41B, ZNF143, CLEC16A, DENND5A, WEE1, SWAP70, SCUBE2, NRIP3, TMEM9B-AS1, SBF2-AS1 
	   RMI2, TMEM9B, C11orf16, SOCS1, SBF2, LITAF, ADM, SNN, AMPD3, TXNDC11 
Negative:  KLHL28, C14orf28, PRPF39, LRFN5, FKBP3, FBXO33, FANCM, MIA2, MIS18BP1, PNN 
	   RPL10L, TRAPPC6B, GEMIN2, MDGA2, LINC00648, SEC23A, RPS29, AL139099.1, MIPOL1, LRR1 
	   RPL36AL, MGAT2, DNAAF2, SLC25A21, POLE2, KLHDC2, NEMF, WDR92, AL627171.1, C1D 
PC_ 5 
Positive:  POLD3, PGM2L1, LIPT2, KCNE3, SPCS2, RNF169, XRRA1, NEU3, P4HA3, SLCO2B1 
	   ARRB1, PPME1, RPS3, C2CD3, KLHL35, UCP2, GDPD5, DNAJB13, SERPINH1, PAAF1 
	   MAP6, COA4, DGAT2, MRPL48, UVRAG, RAB6A, WNT11, LRRC32, PLEKHB1, TSKU 
Negative:  TSN, CLASP1, TFCP2L1, RALB, TMEM185B, EPB41L5, PTPN4, TMEM177, TMEM37, DBI 
	   C2orf76, STEAP3, EN1, INSIG2, CCDC93, DDX18, ARID3B, UBL7-AS1, UBL7, SEMA7A 
	   CYP11A1, CCDC33, STRA6, CLK3, DPP10, ISLR, EDC3, PML, STOML1, CYP1A1 
Computing nearest neighbor graph
Computing SNN
Warning: Data is of class matrix. Coercing to dgCMatrix.
Finding variable features for layer counts
Calculating gene variances
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Centering and scaling data matrix

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PC_ 1 
Positive:  ZNF280D, TCF12, LINC00926, MNS1, CGNL1, TEX9, MYZAP, POLR2M, RFX7, ALDH1A2 
	   NEDD4, PRTG, LIPC, PYGO1, ADAM10, RNF111, SLTM, CCNB2, MYO1E, FAM81A 
	   GCNT3, GTF2A2, BNIP2, ANXA2, RORA, VPS13C, TLN2, AC103740.1, TPM1, LACTB 
Negative:  ILVBL, SLC1A6, OR7C1, NOTCH3, ZNF333, EPHX3, NDUFB7, BRD4, TECR, AKAP8 
	   DNAJB1, AKAP8L, GIPC1, WIZ, UCA1, TPM4, PKN1, RAB8A, TMEM199, POLDIP2 
	   FAM32A, VTN, AP1M1, DDX39A, SARM1, KLF2, EPS15L1, SLC46A1, C19orf44, KXD1 
PC_ 2 
Positive:  RIN3, LGMN, CPSF2, UBTD1, NDUFB1, MMS19, GOLGA5, ATXN3, ZDHHC16, ANKRD2 
	   EXOSC1, PI4K2A, TRIP11, PGAM1, CHGA, MORN4, RRP12, FBLN5, AVPI1, ITPK1 
	   FRAT2, TC2N, MARVELD1, ITPK1-AS1, CCDC88C, ZFYVE27, FRAT1, MOAP1, RPS6KA5, TTC7B 
Negative:  ZDHHC11, ZDHHC11B, AC026740.1, BRD9, TPPP, TRIP13, AC116351.1, NUDT2, CEP72, NKD2 
	   KIF24, SLC9A3, SLC12A7, EXOC3, UBAP1, SLC6A19, AHRR, TERT, DCAF12, PDCD6 
	   CLPTM1L, XPOT, UBAP2, C12orf56, TBK1, RASSF3, SLC6A3, C12orf66, GNS, UBE2R2 
PC_ 3 
Positive:  TTC14, USP13, CCDC39, FXR1, NDUFB5, MRPL47, DNAJC19, ACTL6A, SOX2, GNB4 
	   MFN1, ATP11B, ZNF639, KCNMB3, DCUN1D1, PIK3CA, MCCC1, ZMAT3, LAMP3, LINC00501 
	   MCF2L2, TBL1XR1, B3GNT5, NAALADL2, KLHL24, YEATS2, NLGN1, FAM72A, CTSE, AC131160.1 
Negative:  PHACTR1, EDN1, HIVEP1, TBC1D7, ADTRP, GFOD1, NEDD9, ERVFRD-1, SMIM13, ELOVL2 
	   GCM2, MAK, SYCP2L, TMEM14B, TMEM14C, PAK1IP1, C6orf52, GCNT2, TFAP2A, SLC35B3 
	   EEF1E1, EEF1E1-BLOC1S5, BLOC1S5, BLOC1S5-TXNDC5, TXNDC5, BMP6, SNRNP48, DSP, RIOK1, SSR1 
PC_ 4 
Positive:  LRFN4, SYT12, RHOD, PC, KDM2A, ANKRD13D, RCE1, SSH3, C11orf80, POLD4 
	   SPTBN2, CLCF1, RBM4B, RAD9A, RBM4, PPP1CA, RBM14-RBM4, RPS6KB2, RBM14, PTPRCAP 
	   CCS, CORO1B, CCDC87, TMEM134, CTSF, AIP, ACTN3, PITPNM1, ZDHHC24, CDK2AP2 
Negative:  FKBP6, TRIM50, NSUN5, POM121, TYW1B, CALN1, AUTS2, TYW1, SBDS, TMEM248 
	   RABGEF1, CDK17, ELK3, NEDD1, TMPO-AS1, LTA4H, KCTD7, TMPO, HAL, SLC25A3 
	   AMDHD1, TPST1, IKBIP, APAF1, SNRPF, CRCP, ANKS1B, NTN4, UHRF1BP1L, USP44 
PC_ 5 
Positive:  MAP3K4, AGPAT4, QKI, SFT2D1, PLG, MPC1, RPS6KA2, SLC22A3, RNASET2, FGFR1OP 
	   IGF2R, UNC93A, PNLDC1, MRPL18, DACT2, TCP1, ACAT2, SMOC2, WTAP, THBS2 
	   WDR27, SOD2, C6orf120, RSPH3, PHF10, C6orf99, EZR, CDT1, PIEZO1, SYTL3 
Negative:  S100PBP, KIAA1522, YARS, FNDC5, TMEM54, RNF19B, AK2, TRIM62, ZNF362, AL513327.1 
	   PHC2, ZSCAN20, SMIM12, GJB5, GJB3, GJA4, DLGAP3, ZMYM6, ZMYM1, SFPQ 
	   PSMB2, ZMYM4, C1orf216, NCDN, CLSPN, KIAA0319L, AGO4, AGO1, AGO3, ZFP57 
Computing nearest neighbor graph
Computing SNN
Warning: Data is of class matrix. Coercing to dgCMatrix.
Finding variable features for layer counts
Calculating gene variances
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Centering and scaling data matrix

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PC_ 1 
Positive:  GMFB, CNIH1, CGRRF1, SAMD4A, CDKN3, GCH1, BMP4, WDHD1, DDHD1, SOCS4 
	   MAPK1IP1L, FERMT2, GNPNAT1, LGALS3, STYX, DLGAP5, PSMC6, FBXO34, FANCL, ATG14 
	   GPR137C, VRK2, TBPL2, EFEMP1, TXNDC16, KTN1-AS1, PNPT1, NID2, CCDC88A, GNG2 
Negative:  LRFN4, PC, SYT12, RCE1, C11orf80, RHOD, SPTBN2, KDM2A, ANKRD13D, RBM4B 
	   RBM4, SSH3, RBM14-RBM4, POLD4, RBM14, CLCF1, CCS, CCDC87, RAD9A, CTSF 
	   ACTN3, ZDHHC24, PPP1CA, BBS1, DPP3, RPS6KB2, PELI3, MRPL11, PTPRCAP, SLC29A2 
PC_ 2 
Positive:  RPS5, AC012313.1, ZNF584, ZNF132, ZNF324B, ZNF324, ZNF446, NOP10, NUTM1, LPCAT4 
	   SLC12A6, GOLGA8A, EMC4, KATNBL1, PGBD4, EMC7, FSIP1, GPR176, DEK, EIF2AK4 
	   SRP14, SRP14-AS1, BMF, BUB1B, PAK6, PLCB2, ZNF530, KNSTRN, ZIK1, ZNF416 
Negative:  SLC35C2, CD40, ELMO2, NCOA5, ZNF334, SLC12A5, MMP9, SLC13A3, ZNF335, TP53RK 
	   PCIF1, SLC2A10, PLTP, EYA2, CTSA, ZMYND8, NEURL2, AL031666.2, ZSWIM1, NCOA3 
	   SULF2, ZSWIM3, PREX1, ACOT8, ARFGEF2, SNX21, CSE1L, TNNC2, STAU1, UBE2C 
PC_ 3 
Positive:  NR1H3, MADD, SLC39A13, ACP2, PSMC3, DDB2, PACSIN3, CELF1, NDUFS3, ARFGAP2 
	   C11orf49, PTPMT1, LRP4, CKAP5, KBTBD4, F2, ZNF408, C1QTNF4, ARHGAP1, ATG13 
	   MTCH2, EIF3I, MARCKSL1, LCK, HDAC1, TMEM234, BSDC1, IQCC, FAM229A, SDHC 
Negative:  ATRNL1, GFRA1, TRUB1, C10orf82, HSPA12A, FAM160B1, ENO4, ABLIM1, SLC18A2, AFAP1L2 
	   PDZD8, TDRD1, RAB11FIP2, NHLRC2, CASC2, NMD3, B3GALNT1, DCLRE1A, PPM1L, SPTSSB 
	   FAM204A, BCHE, KPNA4, TRIM59, ZBBX, SMC4, PDCD10, IFT80, CASP7, PRLHR 
PC_ 4 
Positive:  DCLRE1A, CASP7, NHLRC2, NRAP, TDRD1, HABP2, TCF7L2, AFAP1L2, ABLIM1, VTI1A 
	   ZDHHC6, FAM160B1, ACSL5, TRUB1, ATRNL1, GPAM, GFRA1, SHOC2, C10orf82, HSPA12A 
	   BBIP1, ENO4, SLC18A2, PDCD4, PDZD8, RAB11FIP2, CASC2, SMC3, FAM204A, PRLHR 
Negative:  HBP1, PRKAR2B, COG5, CCDC71L, NAMPT, GPR22, SYPL1, CDHR3, ATXN7L1, DUS4L 
	   EFCAB10, RINT1, PUS7, BCAP29, SRPK2, CBLL1, KMT2E, KMT2E-AS1, LINC01004, LHFPL3 
	   ORC5, DLD, RELN, PSMC2, LAMB1, DNAJC2, PMPCB, NRCAM, NAPEPLD, ARMC10 
PC_ 5 
Positive:  LAMA4, FAM229B, RFPL4B, MARCKS, TUBE1, FYN, HDAC2, FRK, TRAF3IP2, DSE 
	   TSPYL4, TSPYL1, NT5DC1, RWDD1, RSPH4A, TRAF3IP2-AS1, KPNA5, FAM162B, REV3L, ROS1 
	   GOPC, DCBLD1, SLC16A10, NUS1, SLC35F1, CEP85L, RPF2, MCM9, GTF3C6, AMD1 
Negative:  PNLDC1, PLG, SLC22A3, IGF2R, MAP3K4, MRPL18, AGPAT4, TCP1, QKI, ACAT2 
	   SFT2D1, WTAP, SOD2, RSPH3, MPC1, RPS6KA2, C6orf99, RNASET2, EZR, SYTL3 
	   FGFR1OP, DYNLT1, UNC93A, TMEM181, DACT2, TULP4, SMOC2, GTF2H5, THBS2, SERAC1 
Computing nearest neighbor graph
Computing SNN
Warning: Data is of class matrix. Coercing to dgCMatrix.
Finding variable features for layer counts
Calculating gene variances
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Centering and scaling data matrix

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PC_ 1 
Positive:  PITPNM1, AIP, CDK2AP2, TMEM134, GSTP1, CORO1B, PTPRCAP, NDUFV1, RPS6KB2, NUDT8 
	   PPP1CA, ACY3, RAD9A, CLCF1, ALDH3B2, POLD4, SSH3, UNC93B1, ANKRD13D, KDM2A 
	   RHOD, NDUFS8, SYT12, LRFN4, TCIRG1, PC, RCE1, C11orf80, CHKA, SPTBN2 
Negative:  TBPL2, KTN1-AS1, ATG14, FBXO34, DLGAP5, LGALS3, MAPK1IP1L, SOCS4, WDHD1, GCH1 
	   FERMT2, DDHD1, SAMD4A, BMP4, GNPNAT1, CGRRF1, GMFB, CNIH1, CDKN3, STYX 
	   PSMC6, GPR137C, TXNDC16, NID2, GNG2, FRMD6, TMX1, ABHD12B, PYGL, NIN 
PC_ 2 
Positive:  IMPA2, TUBB6, MPPE1, RAB31, CHMP1B, VAPA, GNAL, NAPG, AFG3L2, PPP4R1 
	   SPIRE1, RALBP1, AP005482.1, TWSG1, PSMG2, ANKRD12, CEP76, NDUFV2, RAB12, PTPN2 
	   PTPRM, SEH1L, LAMA1, CEP192, LINC00668, LDLRAD4, ARHGAP28, TMEM200C, FAM210A, EPB41L3 
Negative:  U2AF2, CCDC106, EPN1, NLRP9, ZNF865, ZNF784, ZNF581, ZNF580, RFPL4A, ZNF524 
	   RFPL4AL1, FIZ1, NLRP11, NAT14, NLRP4, ZNF628, ISOC2, NLRP13, UBE2S, NLRP8 
	   RPL28, NLRP5, COX6B2, TMEM150B, ZNF787, HSPBP1, ZNF444, PPP6R1, TMEM86B, ZSCAN5B 
PC_ 3 
Positive:  ZCCHC14, JPH3, AC010536.1, KLHDC4, SLC7A5, BANP, ZFPM1, ZC3H18, CYBA, MVD 
	   SNAI3-AS1, SNAI3, RNF166, CTU2, PIEZO1, CDT1, MRPS31, FOXO1, COG6, SYCE1 
	   NHLRC3, CYP2E1, SPRN, MTG1, PAOX, ECHS1, FUOM, PRAP1, ZNF511, TUBGCP2 
Negative:  DEPDC5, PISD, YWHAH, PRR14L, RFPL2, SFI1, RFPL3S, RTCB, EIF4ENIF1, FBXO7 
	   DRG1, SYN3, PATZ1, TIMP3, PIK3IP1, HMGXB4, LIMK2, RNF185, TOM1, PLA2G3 
	   INPP5J, HMOX1, SMTN, PDLIM7, DBN1, MORC2, MORC2-AS1, DOK3, PRR7, OSBP2 
PC_ 4 
Positive:  RGS7, GREM2, FMN2, CHRM3, ZP4, RYR2, MTR, ACTN2, HEATR1, LGALS8 
	   EDARADD, GPR137B, NID1, LYST, GNG4, B3GALNT2, TBCE, GGPS1, ARID4B, RBM34 
	   C6orf203, OVCH1-AS1, TMTC1, ERGIC2, IPO8, FAR2, CCDC91, PTHLH, CAPRIN2, MRPS35 
Negative:  EPB41L1, AL121895.1, CNBD2, AAR2, DLGAP4, MYL9, SOGA1, SAMHD1, TGIF2, DSN1 
	   RBL1, SCAND1, SLA2, NDRG3, MROH8, PHF20, RPN2, RBM39, MANBAL, ROMO1 
	   SRC, BLCAP, NFS1, NNAT, RBM12, CTNNBL1, CPNE1, SPAG4, TTI1, ERGIC3 
PC_ 5 
Positive:  PPIL6, CD164, SMPD2, CEP57L1, MICAL1, SESN1, ZBTB24, ARMC2, AK9, FOXO3 
	   FIG4, WASF1, SNX3, CDC40, NR2E1, CDK19, OSTM1, AMD1, SEC63, GTF3C6 
	   SOBP, RPF2, PDSS2, SLC16A10, BEND3, REV3L, C6orf203, FDPS, RUSC1-AS1, PKLR 
Negative:  ZNF326, LRRC8D, LRRC8C, ZNF644, TOMM20, CDC7, LRRC8B, IRF2BP2, RBM34, ARID4B 
	   TGFBR3, GBP4, TARBP1, GGPS1, BRDT, TBCE, COA6, GBP2, B3GALNT2, EPHX4 
	   GNG4, BTBD8, SLC35F3, LYST, GBP1, C1orf146, KCNK1, GLMN, NID1, RBMXL1 
Computing nearest neighbor graph
Computing SNN
Warning: Data is of class matrix. Coercing to dgCMatrix.
Finding variable features for layer counts
Calculating gene variances
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Centering and scaling data matrix

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PC_ 1 
Positive:  CADM2, CHMP2B, GBE1, CGGBP1, ROBO2, ZNF717, ABI3BP, TFG, LINC00960, ZNF654 
	   ROBO1, TMEM45A, CRYBG3, GABRR3, LNP1, ARL6, MTRNR2L12, C3orf38, FRG2C, CLDND1 
	   SENP7, NSUN3, NIT2, PROS1, ARL13B, FILIP1L, TBC1D23, CPOX, RPL24, ZBTB11-AS1 
Negative:  XKR8, SMPDL3B, RPA2, PPP1R8, THEMIS2, EYA3, STX12, FAM76A, PTAFR, IFI6 
	   DNAJC8, SESN2, AHDC1, MED18, WASF2, GPR3, PHACTR4, MAP3K6, RCC1, RMDN3 
	   SYTL1, TMEM222, RAD51, TTLL1, TRNAU1AP, WDTC1, BIK, ZNFX1, ZFAS1, KCNB1 
PC_ 2 
Positive:  SPAST, SLC30A6, LCLAT1, NLRC4, LBH, YPEL5, AC016907.2, YIPF4, CLIP4, BIRC6 
	   WDR43, TTC27, TRMT61B, LTBP1, SPDYA, RASGRP3, FAM98A, PPP1CB, CRIM1, PLB1 
	   FEZ2, FOSL2, VIT, RBKS, MRPL33, STRN, SLC4A1AP, HEATR5B, SUPT7L, GPN1 
Negative:  AGAP4, ZFAND4, TPBG, MARCH8, UBE3D, IBTK, BCKDHB, ALOX5, DOPEY1, ZNF22 
	   TTK, PGM3, C10orf25, RWDD2A, RASSF4, CXCL12, ZNF32, ELOVL4, ZNF485, ME1 
	   ZNF239, ZNF487, PRSS35, SH3BGRL2, HNRNPF, LCA5, FXYD4, HMGN3-AS1, RASGEF1A, HMGN3 
PC_ 3 
Positive:  RPL10L, MDGA2, MIS18BP1, LINC00648, FANCM, RPS29, FKBP3, AL139099.1, PRPF39, LRR1 
	   KLHL28, RPL36AL, C14orf28, MGAT2, LRFN5, DNAAF2, FBXO33, POLE2, MIA2, KLHDC2 
	   PNN, NEMF, TRAPPC6B, AL627171.1, GEMIN2, ARF6, SEC23A, VCPKMT, MIPOL1, SOS2 
Negative:  NR2F6, OCEL1, USE1, MYO9B, HAUS8, RAB24, NSD1, MXD3, PRELID1, FGFR4 
	   LMAN2, CPAMD8, ZNF346, RGS14, F12, GRK6, UIMC1, SIN3B, PRR7, TSPAN17 
	   TMEM38A, DBN1, EIF4E1B, PDLIM7, SMIM7, SNCB, DOK3, MED26, GPRIN1, DDX41 
PC_ 4 
Positive:  SYNM, IGF1R, TTC23, ARRDC4, LRRC28, NR2F2, MEF2A, NR2F2-AS1, LYSMD4, MCTP2 
	   ADAMTS17, RGMA, ASB7, CHD2, ALDH1A3, FAM174B, ETFA, NRG4, LRRK1, FBXO22 
	   ST8SIA2, PYGO1, C15orf65, CCPG1, UBE2Q2, PIGB, CHSY1, SLCO3A1, RAB27A, SNX33 
Negative:  SYT13, PRDM11, SLC35C1, TP53I11, CRY2, MAPK8IP1, TSPAN18, PEX16, PHF21A, CREB3L1 
	   DGKZ, CD82, MDK, AMBRA1, HARBI1, EXT2, ATG13, ACCSL, ARHGAP1, C11orf96 
	   ZNF408, ALKBH3, F2, CKAP5, HSD17B12, LRP4, C11orf49, PACSIN3, ARFGAP2, DDB2 
PC_ 5 
Positive:  DBN1, PDLIM7, PRR7, GRK6, B4GALT7, TMED9, DOK3, FAM193B, DDX41, F12 
	   N4BP3, RMND5B, RGS14, NHP2, HNRNPAB, PHYKPL, COL23A1, LMAN2, CLK4, ZNF354A 
	   ZNF354B, ZFP2, ZNF454, PRELID1, ZNF879, ZNF354C, MXD3, RAB24, NSD1, FGFR4 
Negative:  ZNF256, C19orf18, ZNF135, ZNF606, ZSCAN18, ZNF329, ZNF274, ZNF544, ZNF8, ERVK3-1 
	   AC020915.1, ZNF154, ZNF551, ZSCAN4, ZNF211, AC010642.2, ZNF134, ZNF530, ZSCAN22, ZIK1 
	   ZNF416, A1BG, ZNF550, ZNF549, A1BG-AS1, ZNF773, ZNF419, ZNF497, ZNF772, ZNF749 
Computing nearest neighbor graph
Computing SNN
Warning: Data is of class matrix. Coercing to dgCMatrix.
Finding variable features for layer counts
Calculating gene variances
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Centering and scaling data matrix

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PC_ 1 
Positive:  GNB5, MYO5C, BCL2L10, MYO5A, ARPP19, FAM214A, WDR72, DUOX1, UNC13C, DUOXA1 
	   SHF, GATM, SPATA5L1, SORD, SLC30A4, RSL24D1, BLOC1S6, SEMA6D, MYEF2, DUT 
	   TRIM69, RAB27A, EID1, FBN1, CEP152, PIGB, SECISBP2L, COPS2, GALK2, B2M 
Negative:  CKAP5, LRP4, F2, C11orf49, ZNF408, ARFGAP2, ARHGAP1, PACSIN3, ATG13, DDB2 
	   ACP2, HARBI1, NR1H3, AMBRA1, MDK, MADD, SLC39A13, DGKZ, CREB3L1, PHF21A 
	   PEX16, MAPK8IP1, CRY2, SLC35C1, SYT13, RAD23A, GADD45GIP1, AC092069.1, CALR, FARSA 
PC_ 2 
Positive:  ABCC6, NOMO3, ABCC1, XYLT1, NOMO2, FOPNL, RPS15A, ARL6IP1, MYH11, SMG1 
	   NDE1, TMC7, C16orf45, DTL, MPV17L, COQ7, RRN3, PPP2R5A, ITPRIPL2, NTAN1 
	   TMEM206, PDXDC1, SYT17, NPIPA1, NENF, TMC5, NOMO1, GDE1, ATF3, PLA2G10 
Negative:  APOBEC3B, CBX6, DNAL4, SUN2, AL021707.2, GTPBP1, JOSD1, TOMM22, CBY1, FAM227A 
	   DMC1, DDX17, KDELR3, SCAMP5, PPCDC, RPP25, C15orf39, COMMD4, COX5A, NEIL1 
	   FAM219B, MAN2C1, MPI, SIN3A, CSNK1E, SCAMP2, PTPN9, ULK3, SNUPN, TMEM184B 
PC_ 3 
Positive:  PHLPP2, AP1G1, MARVELD3, ATXN1L, CHST4, IST1, ZNF821, ZNF19, PKD1L3, ZNF23 
	   DHODH, ZFHX3, PMFBP1, TXNL4B, DHX38, PSMD7, NPIPB15, GLG1, RFWD3, MLKL 
	   NAGPA, C16orf89, ALG1, RBFOX1, FA2H, PPL, METTL22, ABAT, TMEM186, PMM2 
Negative:  APBB2, UCHL1, LIMCH1, TMEM33, DCAF4L1, SLC30A9, BEND4, SHISA3, ATP8A1, GUF1 
	   GNPDA2, COX7B2, COMMD8, ATP10D, NFXL1, NIPAL1, ZNF490, ZNF564, ZNF443, ZNF799 
	   ZNF709, ZNF791, ZNF442, MAN2B1, ZNF625-ZNF20, ZNF625, ZNF20, CNGA1, ZNF563, ZNF44 
PC_ 4 
Positive:  USP18, DGCR2, TUBA8, SLC25A1, PEX26, MRPL40, CLTCL1, CDC45, C22orf39, HIRA 
	   MICAL3, CLDN5, GP1BB, GNB1L, TXNRD2, COMT, TANGO2, DGCR8, TRMT2A, RANBP1 
	   ZDHHC8, RTN4R, DGCR6L, FGFR1OP2, TM7SF3, ITPR2, AC024896.1, RASSF8, MED21, RASSF8-AS1 
Negative:  ZNF688, ZNF785, MMP15, KIFC3, ZNF689, TEPP, USB1, KATNB1, PRR14, TMEM106A 
	   FBRS, NBR1, LINC00910, BRCA1, ARL4D, SRCAP, DHX8, ETV4, RND2, MPP3 
	   DUSP3, MPP2, PPY, DEK, VAT1, PHKG2, PYY, IFI35, NAGS, RNF40 
PC_ 5 
Positive:  FIP1L1, LNX1, SCFD2, CHIC2, RASL11B, PDGFRA, ERVMER34-1, KIT, USP46, KDR 
	   SPATA18, SRD5A3, SGCB, TMEM165, CLOCK, DCUN1D4, PDCL2, OCIAD2, NMU, EXOC1 
	   OCIAD1, CEP135, FRYL, KIAA1211, ZAR1, AASDH, SLC10A4, C6orf99, EZR, SYTL3 
Negative:  SLC7A9, TDRD12, NUDT19, ANKRD27, PDCD5, DPY19L3, ZNF507, TSHZ3, URI1, CCNE1 
	   C19orf12, PLEKHF1, POP4, ZNF101, ATP13A1, GMIP, C6orf203, BEND3, LPAR2, PDSS2 
	   SOBP, SEC63, OSTM1, NR2E1, SNX3, FOXO3, ARMC2, SESN1, CEP57L1, CD164 
Computing nearest neighbor graph
Computing SNN
Warning: Data is of class matrix. Coercing to dgCMatrix.
Finding variable features for layer counts
Calculating gene variances
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Centering and scaling data matrix

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PC_ 1 
Positive:  PPP2R3A, MSL2, EPHB1, CEP63, PCCB, ANAPC13, ELMOD1, SLN, AMOTL2, ALKBH8 
	   STAG1, SLC35F2, CWF19L2, SLC35G2, RYK, RAB39A, IER5, CUL5, NCK1, GLUL 
	   SLCO2A1, ACAT1, RNASEL, IL20RB, NPAT, RGS16, RAB6B, ATM, CLDN18, RGS8 
Negative:  ALDH6A1, LIN52, ABCD4, VRTN, NPC2, ISCA2, LTBP2, AREL1, FCF1, YLPM1 
	   DLST, RPS6KL1, PGF, EIF2B2, MLH3, ACYP1, ZC2HC1C, NEK9, ZNF442, ZNF799 
	   ZNF563, ZNF443, ZNF44, ZNF709, TMED10, ZNF136, ZNF564, ZNF625, ZNF490, ZNF791 
PC_ 2 
Positive:  TSPYL1, RWDD1, DSE, RSPH4A, KPNA5, TSPYL4, FAM162B, ROS1, NT5DC1, GOPC 
	   DCBLD1, FRK, NUS1, HDAC2, SLC35F1, MARCKS, CEP85L, MCM9, RFPL4B, ASF1A 
	   LAMA4, FAM184A, FAM229B, MAN1A1, TUBE1, TBC1D32, FYN, TRAF3IP2, GJA1, TRAF3IP2-AS1 
Negative:  PIM3, CRELD2, ALG12, ZBED4, BRD1, C22orf34, FAM19A5, TBC1D22A, CERK, GRAMD4 
	   CELSR1, TRMU, GTSE1, MSX1, TTC38, CYTL1, CDPF1, PPARA, STK32B, WNT7B 
	   ATXN10, EVC2, ASL, CRCP, FBLN1, GUSB, VKORC1L1, ZNF92, ERV3-1, ZNF117 
PC_ 3 
Positive:  FKTN, TMEM38B, ZNF462, RAD23B, KLF4, FAM206A, CTNNAL1, TMEM245, EPB41L4B, PTPN3 
	   AKAP2, EEF1A2, KCNQ2, PPDPF, GMEB2, ARFGAP1, STMN3, NKAIN4, YTHDF1, RTEL1 
	   GID8, RTEL1-TNFRSF6B, DIDO1, C9orf152, TCFL5, COL9A3, OGFR, TXN, MRGBP, UMPS 
Negative:  SPOCK2, CHST3, ASCC1, PSAP, ANAPC16, CDH23, DDIT4, SLC29A3, DNAJB12, UNC5B 
	   MICU1, PCBD1, MCU, SGPL1, ADAMTS14, OIT3, NODAL, PLA2G12B, EIF4EBP2, P4HA1 
	   NUDT13, LRRC20, ECD, PPA1, FAM149B1, SAR1A, DNAJC9, MRPS16, TYSND1, DNAJC9-AS1 
PC_ 4 
Positive:  OPA1, HRASLS, HES1, MB21D2, FGF12, CCDC50, UTS2B, IL1RAP, LINC00884, CLDN1 
	   TMEM44-AS1, TMEM44, LSG1, AC046143.1, FAM43A, XXYLT1, ACAP2, PPP1R2, APOD, MUC20 
	   MUC4, TNK2, TFRC, SLC51A, PCYT1A, TCTEX1D2, TM4SF19, UBXN7, RNF168, PITX1 
Negative:  NOMO2, XYLT1, NOMO3, ABCC6, RPS15A, ABCC1, FOPNL, MYH11, ARL6IP1, NDE1 
	   C16orf45, SMG1, MPV17L, RRN3, TMC7, NTAN1, COQ7, PDXDC1, NPIPA1, NOMO1 
	   PLA2G10, ITPRIPL2, BFAR, PARN, SYT17, MKL2, TMC5, ERCC4, CPPED1, GDE1 
PC_ 5 
Positive:  CNIH3, CAPN2, DNAH14, WDR26, SUSD4, TLR5, TP53BP2, CNIH4, NVL, FBXO28 
	   DEGS1, DISP1, BROX, AIDA, MIA3, TAF1A, SLC35A5, ATP6V1A, GRAMD1C, CCDC80 
	   HHIPL2, ZDHHC23, TIGIT, NAA50, ZBTB20, GAP43, SPICE1, CD200R1, DUSP10, LSAMP 
Negative:  CHRNB1, ZBTB4, FGF11, POLR2A, TMEM102, TNFSF12, NLGN2, TNFSF12-TNFSF13, TMEM256, TNK1 
	   TNFSF13, KCTD11, SENP3, ACAP1, SENP3-EIF4A1, NEURL4, EIF4A1, GPS2, CD68, EIF5A 
	   MPDU1, YBX2, SOX15, SLC2A4, FXR2, CLDN7, ELP5, SAT2, ATP1B2, TP53 
Computing nearest neighbor graph
Computing SNN
Warning: Data is of class matrix. Coercing to dgCMatrix.
Finding variable features for layer counts
Calculating gene variances
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Centering and scaling data matrix

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PC_ 1 
Positive:  EIF3E, RSPO2, ANGPT1, EMC2, OXR1, ZFPM2, TMEM74, MECOM, GOLIM4, TERC 
	   RPS23, ATP6AP1L, LRP12, NUDCD1, TMEM167A, SERPINI1, CKMT2, CKMT2-AS1, ZCCHC9, ATG10 
	   DPYS, ENY2, XRCC4, SSBP2, RASGRF2, ACTRT3, PDCD10, RIMS2, VCAN, MSH3 
Negative:  NNAT, CTNNBL1, BLCAP, MANBAL, SRC, TTI1, RPN2, MROH8, RPRD1B, LBP 
	   RALGAPB, TGM2, ACTR5, RBL1, PPP1R16B, SAMHD1, FAM83D, TGIF2, SOGA1, SLA2 
	   NDRG3, DSN1, MYL9, DHX35, TOP1, DLGAP4, PLCG1, ZHX3, AAR2, LPIN3 
PC_ 2 
Positive:  ZNF446, ZNF324, ZNF324B, ZNF132, ZNF584, AC012313.1, RPS5, ZNF497, A1BG-AS1, A1BG 
	   ZSCAN22, AC010642.2, AC020915.1, ERVK3-1, ZNF8, ZNF544, ZNF274, ZNF329, ZSCAN18, ZNF135 
	   ZNF606, C19orf18, ZNF256, RTEL1, RTEL1-TNFRSF6B, ANKLE1, ABHD8, BABAM1, USHBP1, MRPL34 
Negative:  STAG1, SLC35G2, NCK1, IL20RB, PCCB, CLDN18, DZIP1L, MSL2, DBR1, PPP2R3A 
	   ARMC8, EPHB1, NME9, CEP63, MRAS, ESYT3, ANAPC13, CEP70, FAIM, AMOTL2 
	   PIK3CB, RYK, PRR23A, SLCO2A1, MRPS22, RAB6B, PRR23B, PRR23C, SRPRB, COPB2 
PC_ 3 
Positive:  HTRA4, TM2D2, PLEKHA2, ADAM9, TACC1, ADAM32, FGFR1, LETM2, ADAM18, DDHD2 
	   IDO1, BAG4, ZMAT4, LSM1, SFRP1, STAR, ASH2L, GOLGA7, EIF4EBP1, GINS4 
	   RAB11FIP1, ANK1, BRF2, ERLIN2, KAT6A, ZNF703, AP3M2, UNC5D, PLAT, RNF122 
Negative:  ARMC8, DBR1, DZIP1L, NME9, CLDN18, MRAS, ESYT3, IL20RB, CEP70, FAIM 
	   NCK1, SLC35G2, PIK3CB, STAG1, PRR23A, PCCB, MRPS22, MSL2, SLCO2A1, RYK 
	   AMOTL2, PPP2R3A, EPHB1, PRR23B, ANAPC13, RAB6B, CEP63, PRR23C, SRPRB, COPB2 
PC_ 4 
Positive:  DNAJC13, ACAD11, ACPP, NPHP3, CPNE4, UBA5, MRPL3, NUDT16, TMEM108, NEK11 
	   ASTE1, BFSP2-AS1, ATP2C1, CDV3, PIK3R4, CDKL1, ATP5S, L2HGDH, MAP4K5, SOS2 
	   ATL1, VCPKMT, TOPBP1, SAV1, COL6A6, ARF6, NIN, AL627171.1, TF, PYGL 
Negative:  WAC-AS1, WAC, MPP7, BAMBI, ARMC4, SVIL, RAB18, ACBD5, MTPAP, MASTL 
	   YME1L1, MAP3K8, ANKRD26, ABI1, PDSS1, ZNF438, APBB1IP, MYO3A, ZEB1-AS1, GPR158 
	   ZEB1, THNSL1, ARHGAP12, ENKUR, KIF5B, PRTFDC1, EPC1, ARHGAP21, ITGB1, KIAA1217 
PC_ 5 
Positive:  TULP4, SERAC1, GTF2H5, SYNJ2, TMEM181, SNX9, DYNLT1, ZDHHC14, SYTL3, EZR 
	   TMEM242, C6orf99, RSPH3, ARID1B, SOD2, TFB1M, WTAP, ACAT2, TIAM2, TCP1 
	   SCAF8, MRPL18, CNKSR3, PNLDC1, IGF2R, IPCEF1, SLC22A3, RGS17, PLG, MTRF1L 
Negative:  NEK11, ASTE1, ATP2C1, NUDT16, PIK3R4, MRPL3, COL6A6, CPNE4, COL6A5, ACPP 
	   DNAJC13, TRH, ACAD11, NPHP3, UBA5, TMEM108, TMCC1-AS1, BFSP2-AS1, TMCC1, CDV3 
	   TOPBP1, TF, SRPRB, PUM2, SDC1, RAB6B, LAPTM4A, RHOB, SLCO2A1, PLXND1 
Computing nearest neighbor graph
Computing SNN
Warning: Data is of class matrix. Coercing to dgCMatrix.
Finding variable features for layer counts
Calculating gene variances
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Centering and scaling data matrix

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PC_ 1 
Positive:  ZBTB34, ZBTB43, LMX1B, MVB12B, PBX3, MAPKAP1, GAPVD1, PPDPF, GMEB2, STMN3 
	   HSPA5, EEF1A2, RTEL1, RTEL1-TNFRSF6B, KCNQ2, ARFGAP1, RABEPK, NKAIN4, YTHDF1, PPP6C 
	   GID8, SCAI, DIDO1, GOLGA1, TCFL5, ARPC5L, COL9A3, RPL35, OGFR, WDR38 
Negative:  SNX18, HSPB3, LINC01033, ARL15, NDUFS4, FST, MOCS2, ITGA2, PELO, ITGA1 
	   ISL1, LHFPL2, ARSB, DMGDH, SCAMP1, BHMT2, BHMT, JMY, AP3B1, PARP8 
	   HOMER1, CMYA5, TBCA, MTX3, WDR41, EMB, THBS4, PDE8B, VCAN, SERINC5 
PC_ 2 
Positive:  KIAA2026, RANBP6, UHRF2, GLDC, KDM4C, PTPRD, IFRD1, ZNF277, DOCK4, IMMP2L 
	   DNAJB9, THAP5, PNPLA8, NRCAM, LAMB1, DLD, CBLL1, BCAP29, DUS4L, TBC1D9 
	   RNF150, ZNF330, ELMOD2, IL15, CLGN, INPP4B, RBPMS, SCOC, USP38, GTF2E2 
Negative:  SLC35F6, CENPA, DRC1, HADHB, DPYSL5, HADHA, RAB10, ASXL2, KIF3C, DTNB 
	   MAPRE3, DNMT3A, TMEM214, POMC, AGBL5, EFR3B, OST4, DNAJC27-AS1, KHK, CGREF1 
	   PREB, DNAJC27, SLC5A6, ADCY3, ATRAID, CENPO, CAD, PTRHD1, NCOA1, SLC30A3 
PC_ 3 
Positive:  LMAN2, F12, PRELID1, RGS14, GRK6, MXD3, PRR7, DBN1, RAB24, PDLIM7 
	   DOK3, NSD1, DDX41, FGFR4, FAM193B, ZNF346, TMED9, UIMC1, B4GALT7, TSPAN17 
	   N4BP3, EIF4E1B, SNCB, RMND5B, GPRIN1, NHP2, RNF44, HNRNPAB, FAF2, PHYKPL 
Negative:  CNOT2, MYRFL, RAB3IP, CCT2, FRS2, YEATS4, LYZ, CPSF6, CPM, MDM2 
	   SLC35E3, NUP107, RAP1B, MDM1, DYRK2, CAND1, GRIP1, HELB, IRAK3, TMBIM4 
	   LLPH, HMGA2, MSRB3, LEMD3, PDZRN4, GXYLT1, SLC2A13, TBC1D30, C12orf40, YAF2 
PC_ 4 
Positive:  PWP2, DNMT3L, TRAPPC10, AIRE, PFKL, UBE2G2, C21orf2, LRRC3, TRPM2, AP001062.1 
	   SUMO3, PTTG1IP, ITGB2, FAM207A, SLC6A19, SLC12A7, ZDHHC11, BRD9, ZDHHC11B, TRIP13 
	   TPPP, AC026740.1, AC116351.1, NKD2, TERT, CEP72, SLC9A3, EXOC3, AHRR, PDCD6 
Negative:  SF3A1, CCDC157, RNF215, SEC14L2, MTFP1, SEC14L4, PES1, TCN2, SLC35E4, DUSP18 
	   OSBP2, MORC2-AS1, MORC2, SMTN, INPP5J, PLA2G3, RNF185, LIMK2, PIK3IP1, PATZ1 
	   DRG1, EIF4ENIF1, SFI1, PISD, PRR14L, DEPDC5, YWHAH, RFPL2, RFPL3S, RTCB 
PC_ 5 
Positive:  FBXL7, ZNF622, BASP1, ANKH, MYO10, DNAH5, TRIO, CDH18, DAP, ANKRD33B 
	   ROPN1L, CDH12, MARCH6, CMBL, C5orf17, CCT5, CDH10, FAM173B, SNHG18, CDH6 
	   SEMA5A, MIR4458HG, FASTKD3, MTRR, C5orf49, SRD5A1, NSUN2, MED10, LINC01019, IRX2 
Negative:  TRPT1, NUDT22, DNAJC4, ELL3, VEGFB, SERF2, ZNF446, FKBP2, HYPK, ZNF324 
	   ZNF324B, PPP1R14B, ZNF132, MFAP1, ZNF584, PLCB3, WDR76, BAD, FRMD5, GPR137 
	   ESRRA, CASC4, ZNF23, ZNF19, TRMT112, CHST4, MARVELD3, PHLPP2, CTDSPL2, AP1G1 
Computing nearest neighbor graph
Computing SNN
Warning: Data is of class matrix. Coercing to dgCMatrix.
Finding variable features for layer counts
Calculating gene variances
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Centering and scaling data matrix

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PC_ 1 
Positive:  NCALD, RRM2B, GRHL2, ZNF706, UBR5, KLF10, YWHAZ, AZIN1, ATP6V1C1, BAALC 
	   FZD6, PABPC1, CTHRC1, SLC25A32, ANKRD46, DCAF13, RIMS2, RNF19A, DPYS, LRP12 
	   SPAG1, ZFPM2, POLR2K, OXR1, FBXO43, ANGPT1, LINC01030, RSPO2, COX6C, DLGAP5 
Negative:  ZSWIM4, C19orf57, AC008686.1, CC2D1A, C19orf53, MRI1, DCAF15, CCDC130, CACNA1A, RFX1 
	   IER2, IL27RA, STX10, NACC1, PALM3, C19orf67, TRMT1, SAMD1, PRKACA, LYL1 
	   ASF1B, DDX39A, DAND5, PKN1, AC092069.1, GIPC1, DNAJB1, GADD45GIP1, TECR, RAD23A 
PC_ 2 
Positive:  RNF38, GNE, CLTA, CCIN, GLIPR2, RECK, TMEM8B, HINT2, NPR2, RGP1 
	   GBA2, CREB3, TLN1, TPM2, ARHGEF39, CCDC107, RMRP, UBAP1, CD72, KIF24 
	   NUDT2, TESK1, C9orf24, RAD23B, ZNF462, FAM219A, KLF4, TMEM38B, FKTN, FAM206A 
Negative:  SAR1B, SEC24A, CDKN2AIPNL, UBE2B, CDKL3, CAMLG, PPP2CA, DDX46, SKP1, C5orf24 
	   TCF7, VDAC1, TXNDC15, C5orf15, PCBD2, FSTL4, CATSPER3, HSPA4, PITX1, H2AFY 
	   ZCCHC10, NEUROG1, AFF4, CXCL14, SLC25A48, LEAP2, LECT2, UQCRQ, TGFBI, GDF9 
PC_ 3 
Positive:  ZNF273, ZNF138, ZNF117, ERV3-1, ZNF107, ZNF92, ZNF680, VKORC1L1, ZNF736, GUSB 
	   ZNF679, ASL, ZNF727, CRCP, TPST1, ZNF716, KCTD7, CHCHD2, PHKG1, RABGEF1 
	   SUMF2, CCT6A, TMEM248, PSPH, SBDS, MRPS17, TYW1, DSCR4, DSCR8, DYRK1A 
Negative:  LTB4R2, LTB4R, NYNRIN, KHNYN, SDR39U1, STXBP6, NOVA1, FOXG1, PRKD1, G2E3 
	   SCFD1, COCH, STRN3, AP4S1, HECTD1, HEATR5A, AL136418.1, ARSI, CAMK2A, TCOF1 
	   CD74, CDX1, PDGFRB, RPS14, DTD2, SYNPO, MYOZ3, NDST1, CSF1R, RBM22 
PC_ 4 
Positive:  SLC7A11, INPP4B, PCDH18, USP38, PCDH10, GAB1, C4orf33, SMARCA5, SCLT1, PGRMC2 
	   SMARCA5-AS1, LARP1B, GYPE, MFSD8, PLK4, HSPA4L, SLC25A31, INTU, ANKRD50, SPRY1 
	   SPATA5, NUDT6, FGF2, BBS12, ADAD1, CCNA2, EXOSC9, TMEM155, KIAA1109, BBS7 
Negative:  MTFP1, SEC14L2, SEC14L4, PES1, RNF215, TCN2, SLC35E4, DUSP18, CCDC157, OSBP2 
	   SF3A1, MORC2-AS1, MORC2, SMTN, INPP5J, RNF185, PLA2G3, LIMK2, PIK3IP1, PATZ1 
	   DRG1, EIF4ENIF1, SFI1, PISD, PRR14L, DEPDC5, APOBEC3B, YWHAH, CBX6, RFPL2 
PC_ 5 
Positive:  N4BP3, B4GALT7, RMND5B, TMED9, NHP2, HNRNPAB, FAM193B, PHYKPL, COL23A1, DDX41 
	   CLK4, DOK3, ZNF354A, PDLIM7, DBN1, ZNF354B, PRR7, ZFP2, GRK6, SRCAP 
	   PRR14, FBRS, ZNF454, PHKG2, ZNF689, ZNF785, F12, ZNF688, ZNF879, RGS14 
Negative:  SEMA5A, MIR4458HG, FASTKD3, MTRR, EFCAB10, ATXN7L1, RINT1, CDHR3, PUS7, SYPL1 
	   C5orf49, NAMPT, SRPK2, CCDC71L, PRKAR2B, HBP1, KMT2E, COG5, SRD5A1, KMT2E-AS1 
	   GPR22, DUS4L, BCAP29, CBLL1, DLD, LINC01004, NSUN2, LAMB1, NRCAM, LHFPL3 
Computing nearest neighbor graph
Computing SNN
Warning: Data is of class matrix. Coercing to dgCMatrix.
Finding variable features for layer counts
Calculating gene variances
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Centering and scaling data matrix

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PC_ 1 
Positive:  STIP1, MACROD1, OTUB1, COX8A, NAA40, MYBL2, TOX2, JPH2, RCOR2, IFT52 
	   OSER1, MARK2, FITM2, HNF4A, L3MBTL1, TTPAL, SERINC3, PLCG1, ZHX3, C11orf95 
	   TOP1, FAM83D, LPIN3, DHX35, PPP1R16B, PKIG, RTN3, ACTR5, EMILIN3, SRSF6 
Negative:  ACTRT3, TERC, MYNN, LRRC34, MECOM, SEC62, GOLIM4, GPR160, PHC3, SERPINI1 
	   PRKCI, SKIL, PDCD10, CLDN11, RPL22L1, ZBBX, EIF5A2, VCAN, BCHE, TNIK 
	   XRCC4, TMEM167A, PLD1, SPTSSB, ATP6AP1L, FNDC3B, NMD3, RPS23, NCEH1, ATG10 
PC_ 2 
Positive:  DNAJA1, SMU1, APTX, B4GALT1, BAG1, PRSS3, NDUFB6, UBE2R2, CHMP5, NFX1 
	   ANKRD18B, AQP3, NOL6, UBAP2, TOPORS, DDX58, DCAF12, ACO1, UBAP1, KIF24 
	   LINGO2, NUDT2, C9orf24, C9orf72, FAM219A, DNAI1, ENHO, CNTFR, MOB3B, RPP25L 
Negative:  ZNF584, AC012313.1, ZNF132, ZNF324B, RPS5, ZNF324, ZNF497, ZNF446, A1BG-AS1, A1BG 
	   ZSCAN22, AC010642.2, AC020915.1, ERVK3-1, ZNF8, ZNF544, ZNF274, ZNF329, ZSCAN18, ZNF135 
	   ZNF606, C19orf18, ZNF256, PARD6G, ADNP2, RBFADN, TRMT2A, RBFA, TXNL4A, DGCR8 
PC_ 3 
Positive:  DNAAF2, MGAT2, POLE2, RPL36AL, LRR1, KLHDC2, AL139099.1, RPS29, LINC00648, AL627171.1 
	   NEMF, ARF6, L2HGDH, SOS2, VCPKMT, MDGA2, ATP5S, RPL10L, MIS18BP1, CDKL1 
	   FANCM, MAP4K5, FKBP3, PRPF39, ATL1, KLHL28, SAV1, C14orf28, LRFN5, NIN 
Negative:  AC016907.2, YPEL5, LBH, CLIP4, WDR43, LCLAT1, TRMT61B, SPDYA, CAPN13, PPP1CB 
	   PLB1, GALNT14, FOSL2, EHD3, RBKS, MEMO1, MRPL33, DPY30, SLC4A1AP, SPAST 
	   SUPT7L, SLC30A6, GPN1, NLRC4, CCDC121, YIPF4, ZNF512, BIRC6, C2orf16, TTC27 
PC_ 4 
Positive:  TRAF3IP2-AS1, REV3L, TRAF3IP2, SLC16A10, FYN, TUBE1, FAM229B, RPF2, LAMA4, RFPL4B 
	   GTF3C6, MARCKS, AMD1, HDAC2, CDK19, FRK, CDC40, NT5DC1, WASF1, FIG4 
	   TSPYL4, AK9, DSE, ZBTB24, TSPYL1, MICAL1, RWDD1, SMPD2, RSPH4A, PPIL6 
Negative:  DNA2, SLC25A16, TET1, RUFY2, CCAR1, HNRNPH3, STOX1, PBLD, DDX50, DDX21 
	   HERC4, SRGN, SIRT1, VPS26A, DNAJC12, SUPV3L1, CTNNA3, HKDC1, REEP3, HK1 
	   JMJD1C-AS1, TSPAN15, JMJD1C, COL13A1, NRBF2, H2AFY2, EGR2, AIFM2, ADO, TYSND1 
PC_ 5 
Positive:  SCFD2, RASL11B, ERVMER34-1, FIP1L1, LNX1, USP46, SPATA18, CHIC2, SGCB, PDGFRA 
	   DCUN1D4, KIT, OCIAD2, KDR, OCIAD1, SRD5A3, FRYL, TMEM165, ZAR1, CLOCK 
	   SLC10A4, PDCL2, SLAIN2, NMU, TEC, EXOC1, TXK, CEP135, AC107068.1, KIAA1211 
Negative:  GPR176, EIF2AK4, FSIP1, SRP14, THBS1, RASGRP1, SRP14-AS1, FAM98B, SPRED1, BMF 
	   MEIS2, C15orf41, BUB1B, DPH6, PAK6, NANOGP8, PLCB2, ZNF770, KNSTRN, AQR 
	   IVD, ACTC1, BAHD1, GOLGA8B, GOLGA8A, LPCAT4, NUTM1, NOP10, PPA1, SAR1A 
Computing nearest neighbor graph
Computing SNN
Warning: Data is of class matrix. Coercing to dgCMatrix.
Finding variable features for layer counts
Calculating gene variances
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Centering and scaling data matrix

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PC_ 1 
Positive:  RBM12B, TMEM67, TRIQK, PDP1, RUNX1T1, CDH17, GEM, RAD54B, LRRC69, OTUD6B 
	   FSBP, ESRP1, NECAB1, TMEM64, DPY19L4, LINC01030, INTS8, CCNE2, MARCH1, TMA16 
	   NPY1R, TRIM61, NAF1, RAPGEF2, FAM218A, TRIM60, NDUFAF6, TMEM192, KLHL2, MSMO1 
Negative:  MAP1S, FCHO1, COLGALT1, JAK3, RPL18A, PGLS, SLC5A5, SLC27A1, CCDC124, MVB12A 
	   KCNN1, BST2, ARRDC2, PLVAP, MAST3, PIK3R2, GTPBP3, DDA1, IFI30, MRPL34 
	   MPV17L2, ABHD8, RAB3A, ANKLE1, PDE4C, BABAM1, JUND, USHBP1, NR2F6, LSM4 
PC_ 2 
Positive:  KCNK9, TRAPPC9, CHRAC1, COL22A1, AGO2, PTK2, DENND3, SLC45A4, GPR20, PTP4A3 
	   TSNARE1, CYTL1, STK32B, MSX1, EVC2, JRK, EVC, CRMP1, WFS1, PPP2R2C 
	   MAN2B2, MRFAP1, AC093323.1, S100P, MRFAP1L1, BLOC1S4, KIAA0232, TBC1D14, TADA2B, GRPEL1 
Negative:  ZNF610, LHX4, ZNF480, ACBD6, XPR1, ZNF766, ZNF154, ZNF551, ZSCAN4, ZNF211 
	   STX6, ZNF134, ZNF530, MR1, PPP2R1A, ZIK1, IER5, ZNF416, ZNF836, GLUL 
	   ZNF550, RNASEL, ZNF549, ZNF616, RGS16, CDC42SE1, ZNF773, C1orf56, MLLT11, ZNF256 
PC_ 3 
Positive:  KCNK17, KIF6, KCNK5, SAYSD1, DAAM2, GLO1, MOCS1, TDRG1, BTBD9, UNC5CL 
	   ZFAND3, OARD1, CCDC167, APOBEC2, NFYA, CMTR1, TREML2, RNF8, TREM1, TBC1D22B 
	   FOXP4, MDFI, SEMA7A, UBL7, TFEB, UBL7-AS1, CYP11A1, ARID3B, CLK3, CCDC33 
Negative:  PRAMEF10, PRAMEF6, PRAMEF4, PRAMEF5, PRAMEF2, PRAMEF8, HNRNPCL1, PRAMEF9, PRAMEF11, PRAMEF13 
	   PRAMEF1, PRAMEF18, PRAMEF12, PRAMEF15, AADACL3, PRAMEF14, DHRS3, PRAMEF19, PRAMEF17, VPS13D 
	   PRAMEF20, TNFRSF1B, LRRC38, PDPN, TNFRSF8, PRDM2, MIIP, KAZN, MFN2, FAM151A 
PC_ 4 
Positive:  ZNF264, DUXA, AURKC, ZIM3, ZNF805, USP29, ZNF460, PEG3, ZNF543, ZNF304 
	   ZIM2, ZNF547, ZNF835, ZNF548, ZNF17, ZNF71, ZNF749, ZNF772, ZNF470, ZNF419 
	   ZNF773, ZNF549, ZNF550, ZNF416, ZFP28, ZIK1, ZNF530, ZNF134, ZNF211, AC005498.3 
Negative:  GSC, DICER1, DICER1-AS1, CLMN, SYNE3, SNHG10, GLRX5, TCL1B, TCL1A, C14orf132 
	   ATG2B, DLGAP5, LGALS3, GSKIP, FBXO34, MAPK1IP1L, ATG14, AK7, SOCS4, TBPL2 
	   PAPOLA, WDHD1, KTN1-AS1, GCH1, VRK1, SAMD4A, CGRRF1, SETD3, GMFB, CNIH1 
PC_ 5 
Positive:  DUXA, ZIM3, ZNF264, AURKC, USP29, ZNF805, PEG3, ZNF460, ZNF543, ZIM2 
	   ZNF304, ZNF547, ZNF835, ZNF548, ZNF17, ZNF749, ZNF71, ZNF772, ZNF419, ZNF773 
	   ZNF549, ZNF551, ZSCAN4, ZNF550, ZNF211, ZNF134, ZNF154, ZNF416, ZNF470, ZIK1 
Negative:  MGMT, TCERG1L, GLRX3, PPP2R2D, BNIP3, DPYSL4, STK32C, LRRC27, PWWP2B, INPP5A 
	   NKX6-2, UTF1, VENTX, ADAM8, TUBGCP2, ZNF511, PRAP1, PCSK9, USP24, FUOM 
	   DHCR24, PRKAA2, ECHS1, PARS2, MROH7, TTC4, DAB1, MROH7-TTC4, PAOX, FAM151A 
Computing nearest neighbor graph
Computing SNN
Warning: Data is of class matrix. Coercing to dgCMatrix.
Finding variable features for layer counts
Calculating gene variances
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Centering and scaling data matrix

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PC_ 1 
Positive:  ZNF230, ZNF155, ZNF222, ZNF221, ZNF223, ZNF45, ZNF284, ZNF404, ZNF224, ZNF283 
	   ZNF225, LYPD5, ZNF234, KCNN4, ZNF227, ZNF226, ZNF235, SMG9, ZNF233, PLAUR 
	   ZNF112, ZNF428, SRRM5, ZNF285, ZNF576, IRGQ, PINLYP, XRCC1, ZNF229, ZNF180 
Negative:  TUBB6, AFG3L2, IMPA2, MPPE1, SPIRE1, CHMP1B, AP005482.1, GNAL, PSMG2, NAPG 
	   CEP76, VAPA, PTPN2, RAB31, PPP4R1, SEH1L, RALBP1, FAM69A, RPL5, EVI5 
	   MTF2, TMED5, GFI1, TWSG1, CEP192, CCDC18, RPAP2, DR1, FNBP1L, ANKRD12 
PC_ 2 
Positive:  SORBS3, PPP3CC, SCD, BLOC1S2, SEC31B, SLC39A14, CWF19L1, NDUFB8, HIF1AN, CHUK 
	   PIWIL2, MRPL43, ERLIN1, POLR3D, CPN1, DNMBP, PHYHIP, ABCC2, BMP1, COX15 
	   REEP4, CUTC, NUDT18, ENTPD7, SLC25A28, FAM160B2, GOT1, DMTN, CNNM1, CPEB3 
Negative:  SFXN1, HRH2, CPLX2, MSX2, THOC3, C5orf47, SIMC1, CPEB4, KIAA1191, BOD1 
	   ARL10, STC2, NOP16, NKX2-5, HIGD2A, BNIP1, CLTB, CREBRF, FAF2, ATP6V0E1 
	   RNF44, GPRIN1, RPL26L1, SNCB, ERGIC1, EIF4E1B, DUSP1, TSPAN17, SH3PXD2B, UIMC1 
PC_ 3 
Positive:  TMEM134, CORO1B, PTPRCAP, AIP, PITPNM1, RPS6KB2, PPP1CA, CDK2AP2, RAD9A, GSTP1 
	   CLCF1, NDUFV1, POLD4, NUDT8, SSH3, ACY3, ANKRD13D, ALDH3B2, KDM2A, UNC93B1 
	   RHOD, NDUFS8, SYT12, TCIRG1, LRFN4, PC, CHKA, RCE1, C11orf80, C11orf24 
Negative:  C2CD5, ETNK1, SOX5, BCAT1, C12orf77, LRMP, CASC1, KRAS, AC087239.1, RASSF8-AS1 
	   RASSF8, ERP27, ITPR2, ART4, FGFR1OP2, C12orf60, TM7SF3, WBP11, H2AFJ, AC024896.1 
	   THNSL1, ENKUR, PRTFDC1, ARHGAP21, KIAA1217, HIST4H4, OTUD1, MSRB2, MED21, ARMC3 
PC_ 4 
Positive:  RBM14-RBM4, RBM14, RBM4, CCDC87, CCS, CTSF, ACTN3, ZDHHC24, RBM4B, BBS1 
	   DPP3, PELI3, SPTBN2, MRPL11, C11orf80, SLC29A2, RCE1, BRMS1, CD248, PC 
	   YIF1A, LRFN4, CNIH2, RAB1B, SYT12, KLC2, RHOD, PACS1, KDM2A, SF3B2 
Negative:  GID8, YTHDF1, NKAIN4, ARFGAP1, DIDO1, KCNQ2, TCFL5, EEF1A2, COL9A3, PPDPF 
	   OGFR, GMEB2, MRGBP, STMN3, RTEL1, SLCO4A1, RTEL1-TNFRSF6B, GATA5, RBBP8NL, CABLES2 
	   RPS21, RAB11B, MARCH2, HNRNPM, ZNF414, MYO1F, RAB11B-AS1, ZNF558, ANGPTL4, AC010323.1 
PC_ 5 
Positive:  RALGAPB, ACTR5, PPP1R16B, LBP, TGM2, FAM83D, DHX35, TOP1, RPRD1B, PLCG1 
	   NNAT, BLCAP, CTNNBL1, TTI1, ZHX3, SRC, MANBAL, LPIN3, RPN2, EMILIN3 
	   CHD6, MROH8, SRSF6, RBL1, SAMHD1, L3MBTL1, SOGA1, IFT52, DSN1, MYBL2 
Negative:  EPS8L1, PPP1R12C, TNNT1, TNNI3, DNAAF3, PTPRH, AFG3L2, ZCCHC7, SPIRE1, TUBB6 
	   TMEM86B, IMPA2, AP005482.1, MPPE1, PAX5, PSMG2, CHMP1B, PPP6R1, CEP76, LMBR1 
	   MELK, DNAJB6, RNF32, MNX1, UBE3C, NOM1, GNAL, PTPN2, LINC01006, AC073133.2 
Computing nearest neighbor graph
Computing SNN
Warning: Data is of class matrix. Coercing to dgCMatrix.
Finding variable features for layer counts
Calculating gene variances
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Calculating feature variances of standardized and clipped values
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Centering and scaling data matrix

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PC_ 1 
Positive:  VCAN, XRCC4, SNX18, HSPB3, LINC01033, GMPS, SLC33A1, KCNAB1, ARL15, SSR3 
	   TIPARP-AS1, NDUFS4, LACTB2, AC079807.1, EPCAM, MSH2, FBXO11, CALM2, MSH6, KCNK12 
	   FOXN2, TIPARP, PPP1R21, TTC7A, GTF2A1L, STON1-GTF2A1L, STON1, MCFD2, FSHR, AC016722.2 
Negative:  ADORA2A, UPB1, GUCD1, SPECC1L-ADORA2A, SPECC1L, SNRPD3, GGT5, GGT1, SUSD2, PIWIL3 
	   SGSM1, CABIN1, KIAA1671, CRYBB2, LRP5L, DDT, MYO18B, ASPHD2, HPS4, SRRD 
	   DDTL, FOSL1, XRCC1, GSTT2B, JRK, AP000350.7, C11orf68, PINLYP, AP000350.6, DFFB 
PC_ 2 
Positive:  TSKS, AP2A1, FUZ, MED25, PTOV1-AS1, PTOV1, PNKP, SYNGR4, AKT1S1, TMEM143 
	   EMP3, TBC1D17, CARD8, IL4I1, ZNF114, LIG1, NUP62, PLA2G4C, ATF5, VRK3 
	   MED31, TXNDC17, ZNF473, KIAA0753, WSCD1, NLRP1, KCNC3, MIS12, SIMC1, THOC3 
Negative:  TBL1XR1, NAALADL2, NLGN1, ECT2, LINC00501, NCEH1, ZMAT3, FNDC3B, PLD1, PIK3CA 
	   TNIK, KCNMB3, EIF5A2, ZNF639, RPL22L1, MFN1, CLDN11, SKIL, PRKCI, PHC3 
	   SLC35A5, ATG3, CCDC80, BTLA, CD200, CD200R1, GPR160, GCSAM, GTPBP8, C3orf52 
PC_ 3 
Positive:  RASEF, FRMD3, IDNK, UBQLN1, GKAP1, KIF27, C9orf64, HNRNPK, RMI1, SLC28A3 
	   NTRK2, AGTPBP1, NAA35, GOLM1, ISCA1, DAPK1, CTSL, CDK20, SPIN1, NXNL2 
	   C9orf47, S1PR3, CKS2, SECISBP2, SEMA4D, GADD45G, SYK, AUH, NFIL3, ROR2 
Negative:  PHOSPHO1, ABI3, ZNF652, GNGT2, PHB, NGFR, B4GALNT2, SPOP, IGF2BP1, SLC35B1 
	   GIP, FAM117A, KAT7, SNF8, TAC4, UBE2Z, DLX4, CALCOCO2, DLX3, HOXB13 
	   HOXB9, ITGA3, ZNF416, ZNF530, ZNF134, ZIK1, ZNF211, ZNF550, ZSCAN4, ZNF549 
PC_ 4 
Positive:  RAB5C, HSPB9, KAT2A, GHDC, DHX58, NKIRAS2, STAT5B, STAT5A, STAT3, DNAJC7 
	   CNP, ATP6V0A1, TTC25, NAGLU, HSD17B1, ACLY, COASY, MLX, KLHL11, PSMC3IP 
	   TUBG1, NT5C3B, TUBG2, FKBP10, PLEKHH3, CNTNAP1, HAP1, EZH1, RAMP2, GAST 
Negative:  VGF, AP1S1, SERPINE1, TRIM56, MUC12, ZNF628, NAT14, ISOC2, MUC3A, FIZ1 
	   UBE2S, ZNF524, RPL28, ZNF865, ACHE, ARL6IP5, LMOD3, COX6B2, FRMD4B, MITF 
	   FOXP1, EIF4E3, TMEM150B, UFSP1, GPR27, RYBP, HSPBP1, SHQ1, SRRT, CYCS 
PC_ 5 
Positive:  TFAP2A, SLC35B3, CCR7, EEF1E1, IGFBP4, GCNT2, EEF1E1-BLOC1S5, BLOC1S5, BLOC1S5-TXNDC5, SMARCE1 
	   TXNDC5, C6orf52, BMP6, SNRNP48, TOP2A, KRT10, PAK1IP1, DSP, TMEM14C, TMEM99 
	   RIOK1, RARA, TMEM14B, SSR1, KRT23, SYCP2L, MAK, RREB1, CDC6, KRT15 
Negative:  ZNF613, ZNF649, ZNF350, ZNF615, ZNF614, ZNF432, ZNF841, ETFB, ZNF616, VSIG10L 
	   ZNF836, CTU1, PPP2R1A, KLK13, ZNF766, KLK11, KLK10, ZNF480, KLK8, ZNF610 
	   DCAF6, KLK7, MPC2, GPR161, ADCY10, TIPRL, MPZL1, RCSD1, SFT2D2, CREG1 
Computing nearest neighbor graph
Computing SNN

You can check you working directory for the output folders generated by inferCNV.¶

Thank you!¶